CN106844376B

CN106844376B - Method and device for recommending interest points

Info

Publication number: CN106844376B
Application number: CN201510884583.3A
Authority: CN
Inventors: 张冠囡; 凌利强
Original assignee: Autonavi Software Co Ltd
Current assignee: Beijing Gaodeyunxin Technology Co ltd
Priority date: 2015-12-03
Filing date: 2015-12-03
Publication date: 2020-03-03
Anticipated expiration: 2035-12-03
Also published as: CN106844376A

Abstract

The application provides a method and a device for recommending interest points, wherein the method comprises the following steps: responding to an interest point acquisition request from a client, and acquiring longitude and latitude coordinates of the current position of the client; acquiring candidate recommended interest points of the current position of the client according to the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates of each interest point; acquiring the distance from each candidate recommended interest point to the current position of the client and the attribute information of each candidate recommended interest point for representing the characteristics of the candidate recommended interest points; determining a recommended interest point of the current position of the client from the candidate recommended interest points based on the distance from each candidate recommended interest point to the current position of the client and the attribute information of each candidate recommended interest point; and returning the recommended interest points to the client for display. The technical scheme of the application can overcome the defect that the recommended interest point of the position of the user is judged by using a single distance factor.

Description

Method and device for recommending interest points

Technical Field

The present application relates to the field of electronic map navigation, and in particular, to a method and an apparatus for recommending points of interest.

Background

A Point of Interest (POI) is a term in a geographic information system, generally referring to all geographic objects that can be abstracted as points. The point of interest may be a building, a shop, a sight, etc. In the field of electronic maps, points of interest, also called navigation map information, are generally used to mark information of locations where the points actually exist on electronic maps, such as shopping malls, tourist attractions, schools, restaurants, hospitals, supermarkets, and the like. The information of the points of interest generally includes basic attribute information such as names, categories, longitudes and latitudes, and other additional attribute information such as addresses, telephones, floor navigation information, entrance and exit information, and the like.

In the prior art, in order to meet the user requirements (for example, a user cannot intuitively know the environment of the user in an unfamiliar environment and needs to refer to a surrounding interest point to intuitively know the environment of the user), an optimal interest point near the position of the user is recommended to the user. The current way to recommend the best interest point to the user is mainly as follows: and determining an interest point closest to the current position of the user as a recommended interest point, and randomly selecting one of the interest points as the recommended interest point if a plurality of interest points closest to the current position of the user exist. Although the closest recommended interest point can describe the current actual location of the user, the recommended interest point does not necessarily satisfy the requirement of the user for knowing the environment where the user is located, and therefore it is not reasonable to recommend the interest point to the user according to the distance.

Disclosure of Invention

An object of the present application is to provide a method and an apparatus for recommending points of interest, so that the points of interest recommended to a user are more reasonable.

According to an aspect of the present application, there is provided a method of recommending a point of interest, wherein the method includes the steps of: responding to an interest point acquisition request from a client, and acquiring longitude and latitude coordinates of the current position of the client; acquiring candidate recommended interest points of the current position of the client according to the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates of each interest point; obtaining the distance from each candidate recommended interest point to the current position of the client; acquiring attribute information of each candidate recommended interest point for representing the characteristics of the candidate recommended interest points; determining a recommended interest point of the current position of the client from the candidate recommended interest points based on the distance from each candidate recommended interest point to the current position of the client and the attribute information of each candidate recommended interest point; and returning the recommended interest points to the client for display.

According to another aspect of the present application, there is also provided an apparatus for recommending a point of interest, wherein the apparatus includes: the system comprises a latitude and longitude coordinate acquisition unit, a latitude and longitude coordinate acquisition unit and a longitude and latitude coordinate acquisition unit, wherein the latitude and longitude coordinate acquisition unit is used for responding to an interest point acquisition request from a client and acquiring the latitude and longitude coordinate of the current position of the client; the candidate recommended interest point acquisition unit is used for acquiring candidate recommended interest points of the current position of the client according to the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates of each interest point; the distance acquisition unit is used for acquiring the distance from each candidate recommended interest point to the current position of the client; the attribute information acquisition unit is used for acquiring the attribute information of each candidate recommended interest point for representing the characteristics of the candidate recommended interest points; the recommendation interest point determining unit is used for determining the recommendation interest point of the current position of the client from the candidate recommendation interest points based on the distance from each candidate recommendation interest point to the current position of the client and the attribute information of each candidate recommendation interest point; and the return unit is used for returning the recommended interest points to the client for display.

Compared with the prior art, the embodiment of the application has the following advantages: according to the technical scheme, the interest points are recommended according to the distance between the interest points and the current position of the client, the interest points are recommended comprehensively by combining the distance between the interest points and the current position of the client and the attribute information for representing the characteristics of the interest points, so that the interest points recommended to the user are the interest points with the characteristics convenient for the user to identify, the user can identify the environment of the current position of the user better according to the interest points, the defect that the recommended interest points corresponding to the position of the user are judged by adopting a single distance factor in the prior art is overcome, and the recommendation of the interest points is more in line with objective practice.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a flow chart of a method provided by one embodiment of the present application;

fig. 2 is a flowchart of one implementation manner of step S120 in the method provided by an embodiment of the present application;

FIG. 3a schematically shows an example of attribute information of a point of interest;

FIG. 3b schematically shows an example of attribute information of another point of interest

Fig. 4 is a flowchart of step S150 in the method according to an embodiment of the present application;

fig. 5 is a screenshot of a user interface displaying information of a point of interest on a client according to another embodiment of the present application.

FIG. 6 is a flow chart of a method provided by another embodiment of the present application;

FIG. 7 is a schematic illustration of an apparatus provided by an embodiment of the present application;

FIG. 8 is a schematic view of an apparatus according to another embodiment of the present application;

the same or similar reference numbers in the drawings identify the same or similar elements.

Detailed Description

Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.

The term "computer device" or "computer" in this context refers to an intelligent electronic device that can execute predetermined processes such as numerical calculation and/or logic calculation by running predetermined programs or instructions, and may include a processor and a memory, wherein the processor executes a pre-stored instruction stored in the memory to execute the predetermined processes, or the predetermined processes are executed by hardware such as ASIC, FPGA, DSP, or a combination thereof. Computer devices include, but are not limited to, servers, personal computers, laptops, tablets, smart phones, and the like.

The computer equipment comprises user equipment and network equipment. Wherein the user equipment includes but is not limited to computers, smart phones, PDAs, etc.; the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of computers or network servers, wherein Cloud Computing is one of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers. The computer equipment can be independently operated to realize the application, and can also be accessed into a network to realize the application through the interactive operation with other computer equipment in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.

It should be noted that the user equipment, the network device, the network, etc. are only examples, and other existing or future computer devices or networks may also be included in the scope of the present application, if applicable, and are included by reference.

The methods discussed below, some of which are illustrated by flow diagrams, may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium. The processor(s) may perform the necessary tasks.

Specific structural and functional details disclosed herein are merely representative and are provided for purposes of describing example embodiments of the present application. This application may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be termed a second element, and, similarly, a second element may be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

The present application is described in further detail below with reference to the attached figures.

Fig. 1 is a flowchart of a method for determining recommended points of interest according to an embodiment of the present application. The method 1 can be applied to a server side. The method 1 according to the present application comprises at least step S110, step S120, step S130, step S140, step S150 and step S160, as shown in fig. 1.

Step S110, in response to an interest point obtaining request from a client, obtaining longitude and latitude coordinates of a current location of the client.

Wherein the interest point obtaining request is sent by the client terminal after receiving a specific operation triggered by a user (on the client terminal). The specific operation is preset operation that the client automatically sends an interest point acquisition request to the server when triggered.

In a specific embodiment, the specific operation is an operation of opening a specific application on the client by a user. That is, when a user performs an operation of starting a specific application at the client (for example, clicking an icon of the specific application to start the application), the client sends a point of interest acquisition request to the server, so that the point of interest of the current location can be displayed to the user when the specific application is started. For example, the specific application is an electronic map application, when a user opens the electronic map application at a client, the client sends an interest point acquisition request to the server, and displays the acquired interest point information in the electronic map application, and the user can acquire the information of the interest point at the current position when opening the electronic map application.

In another specific embodiment, the specific operation is a specific operation triggered by a user in a specific application. That is, when a user triggers a specific operation for acquiring a point of interest in a specific application installed on a client in the application, the client transmits a point of interest acquisition request to a server in response to the specific operation. In this way, the information of the interest points does not need to be actively pushed to the user, but the user actively acquires the information of the interest points. That is, when the user needs to know the information of the interest point at the current location, the user actively acquires the information by triggering a specific operation. For example, when a user clicks a preset point of interest acquisition button in an electronic map application, the client sends a point of interest acquisition request to the server in response to the operation.

The implementation manner of obtaining the longitude and latitude coordinates of the current position of the client includes, but is not limited to, the following two manners:

(1) sending a positioning request to the client to request the client to return longitude and latitude coordinates of the current position, which are obtained through GPS (global positioning System) or Beidou navigation System positioning; and receiving the latitude and longitude coordinates of the current position returned by the client.

That is, the client performs GPS positioning or beidou navigation system positioning on the current position, and returns the longitude and latitude coordinates of the current position obtained by the positioning. For example, the client locates the current position through a built-in GPS chip and returns the obtained longitude and latitude coordinates. The GPS chip built in the client receives synchronous signals of a plurality of satellites in the sky, and then specific longitude and latitude of the current position are calculated according to the phase difference of the signals.

(2) Sending a wireless local area network information acquisition request to the client to acquire wireless local area network information of a wireless local area network where the client is located currently; receiving wireless local area network information of a current wireless local area network returned by the client; and determining the latitude and longitude coordinates of the current position of the client according to the wireless local area network information.

The wireless local area network where the client is located refers to one or more wireless local area networks (e.g., Wi-FI hotspots) covered by the current location of the client, that is, a wireless hotspot where the client can currently receive signals. The wireless local area network information includes: signal strength, network identification (e.g., SSID), and/or access device identification (e.g., MAC address). And determining the latitude and longitude coordinates of the current position of the client according to the acquired signal strength of one or more wireless local area networks covered by the current position of the client and the network identification and/or the access equipment identification.

In one implementation, the longitude and latitude coordinates of the wireless local area network with the strongest signal strength are determined as the longitude and latitude coordinates of the current position of the client. More specifically, the longitude and latitude coordinates corresponding to the network identifier and/or the access device identifier of the wireless local area network with the highest signal strength in the one or more wireless local area networks covered by the current location of the client may be queried in a pre-established database of correspondence between the network identifier and/or the access device identifier and the longitude and latitude coordinates.

It should be noted that, the foregoing examples are only for better illustrating the technical solutions of the present application, and are not limiting to the present invention, and those skilled in the art should understand that any implementation manner of acquiring the longitude and latitude coordinates of the current location of the client shall be included in the scope of the present invention.

And step S120, acquiring candidate recommended interest points of the current position of the client according to the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates of each interest point.

Step S120 may specifically include step S121 and step S122, as shown in fig. 2.

And step S121, determining a longitude and latitude range according to the longitude and latitude coordinates of the current position of the client and a preset size.

In one embodiment, the preset size includes a preset longitude size and a preset latitude size. And respectively adding and subtracting the longitude coordinate and the longitude size in the longitude and latitude coordinate of the current position of the client to obtain a longitude range of the longitude and latitude range, and respectively adding and subtracting the latitude coordinate and the latitude size in the longitude and latitude coordinate of the current position of the client to obtain a latitude range of the longitude and latitude range. For example, the longitude and latitude coordinates of the current position of the client are (116.3278, 39.9017), and the preset longitude size and the preset latitude size are 0.013 and 0.015 respectively, so that the longitude range can be calculated according to the method and is 116.3148-116.3408, and the latitude range is 39.9012-39.9032.

In another embodiment, the preset dimension is a radius. And calculating a circle by taking the longitude and latitude coordinates of the current position of the client as a central point and the preset size as a radius, wherein the area included by the circle is the longitude and latitude range. For example: the longitude and latitude coordinates of the current position of the client are (116.3278, 39.9017), the preset size is 0.01, and then a circle is drawn by taking (116.3278, 39.9017) as a center point and taking 0.01 as a radius.

In the embodiment of the invention, the preset size is not strictly limited and can be flexibly set according to actual requirements.

And step S122, acquiring each interest point of the longitude and latitude coordinates in the longitude and latitude range to serve as a candidate recommended interest point of the current position of the client.

In step S122, for each point of interest, comparing the longitude of the point of interest with a longitude range in a latitude and longitude range, and comparing the latitude of the point of interest with a latitude range in a latitude and longitude range, and when the longitude of the point of interest is within the longitude range and the latitude thereof is within the latitude range, determining the point of interest as a candidate recommended point of interest.

In step S130, the distance from each candidate recommended interest point to the current location of the client is obtained.

In one embodiment, step S130 specifically includes: acquiring a peripheral longitude and latitude coordinate set of each candidate recommended interest point; under the condition that a peripheral longitude and latitude coordinate set can be obtained, determining the distance from a candidate recommended interest point to the current position of a client according to the longitude and latitude coordinates contained in the peripheral longitude and latitude coordinate set of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client; and under the condition that a peripheral longitude and latitude coordinate set cannot be obtained, determining the distance from the candidate recommended interest point to the current position of the client according to the longitude and latitude coordinates of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client.

The peripheral longitude and latitude coordinate set (AOI, Area of Interest) of the Interest point refers to a longitude and latitude coordinate set used for describing a geographical Area occupied by the Interest point, the longitude and latitude coordinate set comprises a plurality of longitude and latitude coordinates, and the Area described by the longitude and latitude coordinates is the geographical Area occupied by the Interest point. In the POI database, a point of interest is generally abstracted to a point (the point may be a central location point of a geographic area occupied by the point of interest), but actually covers the area of the geographic area, so for some points of interest having a larger area of the geographic area (for example, the area of the geographic area is larger than a preset area threshold), not only the longitude and latitude coordinates of the abstracted point of the point of interest, but also a longitude and latitude coordinate set corresponding to the point of interest are stored in the POI database. For example, the lotus pool park has a large coverage area, and the POI database stores AOI information of the lotus pool park. The position of the interest point is described by the peripheral longitude and latitude coordinate set, and the position of the interest point is far more accurate than the position of the interest point described by the longitude and latitude coordinate set of one point, so that the distance from the candidate recommended interest point to the current position of the client is accurately calculated. In this embodiment, the attribute information of the candidate recommended interest point includes a peripheral longitude and latitude coordinate set of the interest point. The peripheral longitude and latitude coordinate set may be obtained from attribute information of each candidate recommended point of interest. Of course, the peripheral latitude and longitude coordinate set may be obtained from other sources. The attribute information of each candidate recommended point of interest is determined in advance and stored.

Under the condition that a peripheral longitude and latitude coordinate set can be obtained, the step of determining the distance from the candidate recommended interest point to the current position of the client according to the longitude and latitude coordinates contained in the peripheral longitude and latitude coordinate set of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client specifically comprises the following steps: judging whether the longitude and latitude coordinates of the current position of the client are in a polygonal area formed by the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point, if so, the distance from the candidate recommended interest point to the current position of the client is 0; if not, calculating the distance between each longitude and latitude coordinate in the peripheral longitude and latitude coordinate set and the longitude and latitude coordinate of the current position of the client, and determining the minimum distance as the distance from the candidate recommended interest point to the current position of the client.

And judging whether the longitude and latitude coordinates of the current position of the client are in a polygonal area formed by the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point, namely judging whether the longitude and latitude coordinates of the current position of the client are surrounded by the peripheral longitude and latitude coordinate set of the candidate recommended interest point. Specifically, the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point may be connected to form a polygon area, and whether the coordinate of the current location of the client is in the polygon surrounded by the peripheral longitude and latitude coordinates of the interest point is determined by using a method of determining whether the point is inside the polygon, so as to determine whether the coordinate of the current location of the client is in the polygon area formed by the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point.

For example, the sum of the included angles of all sides of a polygon formed by the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point is judged whether to be 360 degrees by using an included angle sum judgment method, and if the sum of the included angles is 360 degrees, the point is in the polygon, namely the longitude and latitude coordinates of the current position of the client are surrounded by the peripheral longitude and latitude coordinate set of the candidate recommended interest point. If the sum of the included angles is not 360 degrees, the point is not positioned outside the polygon, namely, the longitude and latitude coordinates of the current position of the client side are not surrounded by the peripheral longitude and latitude coordinate set of the candidate recommended interest point.

For another example, an injection line method is used to emit a ray from a point determined by the longitude and latitude coordinates of the current position of the client, and the total number of intersection points of all edges of a polygon formed by the ray and the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point is determined to be odd number or even number. If the total number of the intersection points is odd, the point is indicated to be in the polygon, namely the longitude and latitude coordinates of the current position of the client are surrounded by the peripheral longitude and latitude coordinate set of the candidate recommended interest point. If the total number of the intersection points is even, the point is not outside the polygon, namely the longitude and latitude coordinates of the current position of the client are not surrounded by the peripheral longitude and latitude coordinate set of the candidate recommended interest point.

In the case that the peripheral longitude and latitude coordinate set cannot be obtained, the step of determining the distance from the candidate recommended interest point to the current location of the client according to the longitude and latitude coordinates of the candidate recommended interest point and the longitude and latitude coordinates of the current location of the client may specifically include: and calculating the distance from the candidate recommended interest point to the current position of the client according to the longitude and latitude coordinates of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client.

Specifically, the distance between the longitude and latitude coordinates of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client may be calculated by using a common distance calculation formula between two points on the ground. For example, the distance between the longitude and latitude coordinates of the candidate recommended point of interest and the longitude and latitude coordinates of the current location of the client is calculated by using a common distance formula between two points, such as a Haversine formula (half-positive vector formula) or a Great-circle distance formula.

Step S140, obtaining attribute information of each candidate recommended interest point for characterizing the candidate recommended interest point.

The attribute information includes, but is not limited to, a point of interest type, a point of interest weight, a point of interest level, and characteristic attribute information corresponding to the point of interest type.

The types of points of interest include, but are not limited to: train stations, shopping malls, scenic spots.

The interest point weight represents the importance degree of the interest point, and the greater the weight value, the more important the interest point is. The weight represents the importance degree of the interest point, and can be specifically represented by a floating point number between 0 and 1, and the larger the weight value is, the more important the interest point is. The weight of each point of interest is calculated according to a predetermined weight calculation rule.

In one embodiment, the weight of each interest point is determined according to the click rate and the popularity of the network of the interest point. The network popularity is the number of search results obtained by searching the interest points, and the search is performed in a search engine.

Specifically, a floating point number between 0 and 1 can be fitted as the weight of each interest point by using formula (2) based on the click rate and the popularity of the network of the interest point.

p＝k₁v+k₂s (2)

Wherein w is the weight of the point of interest, namely poiweight; p is weighted heat, and can be calculated by formula (2); m is a weighted maximum of heat, M ═ Max (p)_i) I.e. the maximum value among the weighted heat of all POIs participating in the calculation; m is the weighted minimum heat value, m is Min (p)_i) I.e., the smallest value among the weighted heat of all POIs participating in the calculation. In the formula (2), k₁The weighted value of the click quantity v is 0.8; k is a radical of₂The weighted value of the popularity heat of the network is 0.2. v is the click rate of the interest point; s is the popularity of the network of points of interest.

The interest point grade represents the position of the interest point, and the larger the grade value is, the higher the position of the interest point is. The interest point level may specifically be represented by an integer between 0 and 999. Specifically, the rank of each point of interest may be calculated based on a predetermined rank calculation rule according to user attention data (e.g., the click amount of the point of interest, the number of comments, the rank of the point of interest, etc.), known name data (e.g., the search amount of the point of interest, the number of times of being referred to, etc.), landmark data (e.g., the area, the adjacent road rank, etc.) of each point of interest. For example, the rank of each point of interest is calculated using a rank calculation model based on Principal Component Analysis (PCA) or a RankingSVM (rank order algorithm) model, based on one or more of the above data for each point of interest.

The characteristic attribute information corresponding to the interest point type is some characteristic information specific to the interest point type, and generally, the interest point of the interest point type may have the characteristic information commonly. The property attribute information may be set in the POI attribute information in the POI database by:

the method 1 is that for each type of interest point, characteristic attribute information corresponding to the type of interest point is preset, a field corresponding to the characteristic attribute information is set in an attribute information field of the interest point of the type of interest point, different values are given to the field to identify whether the interest point contains the characteristic attribute information, if the field is marked as 1, the interest point contains the characteristic attribute information, and if the field is marked as 0, the interest point does not contain the characteristic attribute information. For example, the attribute information field of the interest point with the interest point type of the train station includes the following characteristic attribute information fields: an entrance field and a ticket office field; the attribute information field of the interest point with the interest point type of the mall comprises the following characteristic attribute information fields: an entrance field, a floor guide field and a shopping guide field; the attribute information field of the interest point with the interest point type of the sight spot comprises the following characteristic attribute information fields: entrance field, admission field, ticket office field and voice guide field.

And 2, setting characteristic attribute information corresponding to each interest point type in advance for each interest point type, setting fields of the characteristic attribute information corresponding to all the interest point types in attribute information fields of all the interest points, giving different values to the fields to identify whether the interest points contain the characteristic attribute information, indicating that the interest points contain the characteristic attribute information if the fields are marked as 1, and indicating that the interest points do not contain the characteristic attribute information if the fields are marked as 0. If it is assumed that the POI includes three POI types (i.e., train station, mall, and sight spot), the following characteristic attribute information fields are set in the attribute information fields of all the points of interest: an entrance field, a ticket office field, an entrance ticket booking field, a voice guide field, a floor guide field and a shopping guide field. Referring to fig. 3a and 3b, attribute information of two points of interest in a POI database is schematically shown, respectively. Wherein, hasEntrace: the entrance field indicates that the POI attribute information includes entrance information if the hasEntrace value is 1, and indicates that the POI attribute information does not include the entrance information if the hasEntrace value is 0; hasSaleWindow: a ticket office field, wherein if the value of hasSaleWindow is 1, the POI attribute information comprises ticket office information, and if the value of hasSaleWindow is 0, the POI attribute information does not comprise ticket office information; hasTickets: booking a ticket field; hasFloorIntro: a floor navigation field; hasshoppengguide: a shopping guide field; hasVoiseGuide: a voice guide field.

Step S150, determining the recommended interest point of the current position of the client from the candidate recommended interest points based on the distance between each candidate recommended interest point and the current position of the client and the attribute information of each candidate recommended interest point.

Detailed flowchart of step S150 in fig. 1 referring to fig. 4, step S150 may specifically include step S151 to step S153.

And step S151, determining candidate recommended interest points to be eliminated according to the distance and/or attribute information from each candidate recommended interest point to the current position of the client.

The attribute information includes the type of the interest point, the weight of the interest point, the grade of the interest point and the characteristic attribute information corresponding to the type of the interest point, and the related content of the characteristic attribute information is detailed in the foregoing content and is not described again here.

Various specific embodiments of step S151 are discussed in detail below.

Embodiment 1:

in embodiment 1, step S151 includes: (a) if the number of the items of the characteristic attribute information contained in the candidate recommended interest point is less than a preset number threshold, determining that the candidate recommended interest point needs to be removed; (b) if the distance between the candidate recommended interest point and the current position of the client is greater than the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs, determining that the candidate recommended interest point needs to be removed; (c) and if the candidate recommended interest point belongs to a preset specific type and the grade of the candidate recommended interest point is less than a preset grade threshold value, determining that the candidate recommended interest point needs to be removed. That is, the candidate recommended interest points can be eliminated as long as any one of the above items (a), (b) and (c) is satisfied.

For (a), specifically:

the characteristic attribute information contained in different interest points is different even for interest points of the same interest point type, although the characteristic attribute information fields contained in the attribute information of the interest points are the same, the characteristic attribute information contained in the interest points is different, such as an interest point a and an interest point B which are of mall types, wherein the characteristic attribute information contained in the interest point a is an entrance and a shopping guide, and the characteristic attribute information contained in the interest point B is an entrance, a floor guide and a shopping guide.

The aforementioned preset number threshold is 2. The candidate recommended interest points as obtained are the interest point "beijing west station" shown in fig. 3a and the interest point "lotus pool park" shown in fig. 3 b. The characteristic attribute information of "beijing west station" shown in fig. 3a includes an entrance and a ticket office, so the number of items of characteristic attribute information included in the point of interest "beijing west station" is not less than a preset number threshold, and the candidate recommended point of interest does not need to be removed. The attribute information of the "lotus pool park" shown in fig. 3b only includes an entrance, so that the number of items of the characteristic attribute information included in the interest point "lotus pool park" is smaller than a preset number threshold, and the candidate recommended interest point needs to be removed.

For (b), specifically:

for example, the normalized distance may be calculated as follows:

ds＝dist/c (3)

wherein c is a normalization constant corresponding to the type of the interest point to which the candidate recommended interest point belongs, namely a distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs. Specifically, when the POI type is a train station, c is 200 meters; when the POI type is the scenic spot, c is 400 meters; when the POI type is mall, c is 50 meters. dist is the distance from the candidate recommended interest point to the current position of the client. And ds is a normalized distance between the candidate recommended interest point and the current position of the client, and is equal to the distance between the candidate recommended interest point and the current position of the client divided by a distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs. Therefore, it can be determined whether the candidate recommended interest point needs to be eliminated by calculating the normalized distance ds and determining whether ds is greater than 1. And ds is greater than 1, which indicates that the distance between the candidate recommended interest point and the current position of the client is greater than the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs, that is, the candidate recommended interest point is farther from the current position of the user, and the candidate recommended interest point is rejected.

For (c), specifically, one or more interest point types may be preset as a specific type, such as the sights may be set as interest points of a specific type, and the level threshold is set as 400. Such as: and if the candidate recommended interest point is the sight spot and the grade of the candidate recommended interest point is less than 400, removing the candidate recommended interest point.

Taking fig. 3 as an example, according to the attribute information shown in fig. 3, the type of the point of interest "beijing west station" is a train station, and does not belong to the preset specific type, so that the candidate recommended point of interest does not need to be removed. The type of the interest point "lotus pool park" is the preset specific type (attraction), and the grade is 912, which is larger than the preset grade threshold 400, therefore, the candidate recommended interest point does not need to be removed.

Embodiment 2:

in embodiment 2, step S151 includes: judging whether the number of items of the characteristic attribute information contained in the candidate recommended interest points is smaller than a preset number threshold, and if so, determining that the candidate recommended interest points need to be removed; if the number of the candidate recommended interest points is not less than the number threshold, judging whether the distance between the candidate recommended interest points and the current position of the client is greater than a distance threshold corresponding to the type of the interest points to which the candidate recommended interest points belong, if so, determining that the candidate recommended interest points need to be removed, if not, judging whether the candidate recommended interest points belong to a preset specific type and the grade of the candidate recommended interest points is less than a preset grade threshold, if so, determining that the candidate recommended interest points need to be removed, and if not, keeping the candidate recommended interest points.

Embodiment 3:

in embodiment 3, step S151 includes: (a) if the number of the items of the characteristic attribute information contained in the candidate recommended interest point is less than a preset number threshold, determining that the candidate recommended interest point needs to be removed; (b) and if the distance between the candidate recommended interest point and the current position of the client is greater than the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs, determining that the candidate recommended interest point needs to be removed. That is, the candidate recommended interest points can be eliminated as long as they satisfy any one of the above items (a) and (b). Compared with embodiment 1, it does not cull recommended interest points according to the rank of candidate recommended interest points of a particular type.

Embodiment 4:

in this embodiment, step S151 includes: (a) if the number of the items of the characteristic attribute information contained in the candidate recommended interest point is less than a preset number threshold, determining that the candidate recommended interest point needs to be removed; (c) and if the candidate recommended interest point belongs to a preset specific type and the grade of the candidate recommended interest point is less than a preset grade threshold value, determining that the candidate recommended interest point needs to be removed. That is, the candidate recommended interest points can be eliminated as long as they satisfy any one of the above items (a) and (c). Compared with the embodiment 1, the method does not eliminate the recommended interest points according to the distance between the candidate recommended interest points and the current position of the client.

Embodiment 5:

in embodiment 5, step S151 includes: (b) if the distance between the candidate recommended interest point and the current position of the client is greater than the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs, determining that the candidate recommended interest point needs to be removed; (c) and if the candidate recommended interest point belongs to a preset specific type and the grade of the candidate recommended interest point is less than a preset grade threshold value, determining that the candidate recommended interest point needs to be removed. That is, the candidate recommended interest points can be eliminated as long as they satisfy any one of the above items (b) and (c). Compared with the embodiment 1, it does not eliminate the recommended interest points according to the number of items of the characteristic attribute information included in the candidate recommended interest points.

Embodiment 6:

in this embodiment 6, instead of simply determining the candidate recommended interest points to be eliminated based on two or three items in the above (a) to (c), a composite index is used, that is, a weighted sum of at least two items of the number of items of the characteristic attribute information included in the candidate recommended interest points, the reciprocal of the distance between the candidate recommended interest points and the current location of the client, and the rank of the candidate recommended interest points.

In this embodiment, step S151 includes: and calculating the weighted sum of at least two items of the number of the characteristic attribute information contained in the candidate recommended interest point, the reciprocal of the distance between the candidate recommended interest point and the current position of the client and the grade of the candidate recommended interest point, and determining whether the candidate recommended interest point needs to be removed or not based on the comparison of the weighted sum and a preset weighted sum threshold.

For example, a · w1+ (1/ds) · w2 is calculated, where a is the number of items of the characteristic attribute information included in the candidate recommended interest point, ds is the distance between the candidate recommended interest point and the current location of the client, w1 and w2 are weights respectively assigned to the number of items a of the characteristic attribute information included in the candidate recommended interest point and the inverse 1/ds of the distance between the candidate recommended interest point and the current location of the client, and w1+ w2 is 1. Assuming that the preset weighted sum threshold is 1.6, w1 is 0.6, w2 is 0.4, a is 1, and ds is 1, a · w1+ (1/ds) · w2 is 1 < 1.6, and the exclusion is not recommended to the user.

Embodiment 7:

in the embodiments 1 to 6, only one kind of attribute information is used to determine the candidate recommended interest points to be eliminated, that is, the number of items of the characteristic attribute information included in the candidate recommended interest points, but actually, other attribute information may be used to determine the candidate recommended interest points to be eliminated, for example, the people stream density. People stream density represents the number of people entering the point of interest per unit of time (e.g., one hour). If the crowd density of a candidate recommended interest point is smaller than the crowd density threshold value, the crowd of the interest point is rare and the candidate recommended interest point is not recommended to the user.

For example, in embodiment 7, step S151 includes: (a) if the people stream density of the candidate recommended interest point is smaller than a preset people stream density threshold value, determining that the candidate recommended interest point needs to be removed; (b) if the distance between the candidate recommended interest point and the current position of the client is greater than the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs, determining that the candidate recommended interest point needs to be removed; (c) and if the candidate recommended interest point belongs to a preset specific type and the grade of the candidate recommended interest point is less than a preset grade threshold value, determining that the candidate recommended interest point needs to be removed. That is, the candidate recommended interest points can be eliminated as long as any one of the above items (a), (b) and (c) is satisfied.

Although the implementation of step S151 is illustrated in embodiments 1 to 7, it should be understood by those skilled in the art that other modifications and variations may be made, and all of them fall within the scope of the present application.

Step S152, aiming at the reserved candidate recommended interest points, determining the recommendation degree of the candidate recommended interest points recommended to the client based on the distance from the candidate recommended interest points to the current position of the client and the attribute information.

The remaining candidate recommended interest points are the remaining candidate recommended interest points which do not need to be eliminated after the step of determining the candidate recommended interest points which need to be eliminated is performed.

In one embodiment, step S152 may specifically include:

determining the ratio of the distance from the candidate recommended interest point to the current position of the client and the distance threshold value corresponding to the type of the interest point to which the candidate recommended interest point belongs as the normalized distance of the candidate recommended interest point; determining the sum of the weight of the candidate recommended interest points and a preset value m as a correction weight, wherein the m is a positive number less than or equal to 1; determining the sum of the normalized distance and a preset n value as a corrected normalized distance, wherein n is a positive number smaller than m; and determining the ratio of the correction weight to the correction normalized distance as the recommendation degree of the candidate recommended interest point recommended to the client.

Specifically, the distance dist from the candidate recommended interest point to the current position of the client, the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs is c, the normalized distance ds of the candidate recommended interest point is dist/c, the weight of the candidate recommended interest point is poiweight, the correction weight is poiweight + m, the correction normalized distance is ds + n, and the recommendation degree of the candidate recommended interest point recommended to the client is as follows:

score＝(poiweight+m)/(ds+n)(4)

for example, the distance dist between the candidate recommended interest point "beijing west station" shown in fig. 3a and the current location of the client is 100 meters, the type is a train station, and the corresponding distance threshold c is 200 meters, then the normalized distance ds between the candidate recommended interest point and the current location of the client is 0.5. The weight of the candidate recommended interest point "beijing west station" is 0.923294, and assuming that m is 1, the modified weight is 0.923294+1, where n may be a positive number approaching 0 infinitely, n < m, where n is 0.01, and the modified normalized distance is 0.51, resulting in a recommendation score of 3.77 for the client that "beijing west station" is recommended. The distance dist between the candidate recommended interest point "lotus pool park" shown in fig. 3b and the current location of the client is 80 meters, the type is a scenic spot, and the corresponding distance threshold c is 500 meters, then the normalized distance ds from the candidate recommended interest point to the current location of the client is 0.16. The weight of the candidate recommended interest point "lotus pool park" is 0.62325, if m is 1, the correction weight is 0.62325+1, and n is 0.01, the correction normalized distance is 0.17, and the recommendation score recommended to the client by the candidate recommended interest point "lotus pool park" is 9.55.

Although one specific form of f (dist) is described above by taking score (poiweight + m)/(ds + n) as an example, f (dist, poiweight) may have other forms, i.e., step S152 may include other processes. For example, the recommendation degree may be calculated by the following formula:

score＝poiweight·p1+(1/(ds+n))p2 (5)

where p1 and p2 are predetermined coefficients, score, poiweight, ds, n have the same meaning as in equation (4). Formula (5) can be used to replace formula (4) because formula (5) also reflects a greater poiweight, a greater recommendation; the smaller ds, the greater the recommendation.

And step S153, selecting the recommended interest points from the reserved candidate recommended interest points according to the determined recommendation degree.

Specifically, the candidate recommended interest point with the highest recommendation degree is used as the recommended interest point. For example, the recommendation score of "beijing west station" recommended to the client is 3.846588, the recommendation score of "lotus pool park" recommended to the client is 10.1453125, and the recommendation score of "lotus pool park" is the highest in the previous step, so "lotus pool park" is taken as the recommended interest point.

Referring to fig. 1, in step S160, the recommended points of interest are returned to the client for presentation.

That is, the name and/or address of the recommended point of interest returned to the client for presentation on a user interface by the client.

Optionally, in order to enable the user to more fully know the detailed information of the recommended point of interest, in a specific implementation manner, this step S160 further includes a step of returning specific attribute information that the recommended point of interest has to the client. For example, if the specific attribute information of the recommended interest point includes entrance information and ticket booking information, the specific attribute information of the recommended interest point is returned to the client. The client receives and displays the name of the recommended interest point and the specific attribute information, such as "ticket booking" information and "entrance and exit" information shown in fig. 5, to the user on the user interface of the application.

According to the technical scheme, the interest points are recommended not only according to the distance between the interest points and the current position of the client, but also by combining the distance between the interest points and the current position of the client and the attribute information for representing the characteristics of the interest points, the interest points recommended to the user are the interest points with the characteristics convenient for the user to identify, therefore, the user can better identify the environment of the current position of the user according to the interest points, the defect that the recommended interest points corresponding to the position of the user are judged by adopting a single distance factor in the prior art is overcome, and the recommendation of the interest points is more reasonable.

Based on the above embodiment, preferably, in order to further improve the efficiency of recommending the interest points, after the step of obtaining the distance from each candidate recommended interest point to the current location of the client (step S130), a step S131 is further included.

Referring to fig. 6, in step S131, it is determined whether the distance from the candidate recommended interest point to the current location of the client is 0.

If yes, go to step S132 to determine the candidate recommended interest points as recommended interest points. And performs step S160. If not, executing the step of obtaining the attribute information of each candidate recommended interest point, namely step S140.

Specifically, in step S130, under the condition that the peripheral longitude and latitude coordinate set can be obtained, the distance from the candidate recommended interest point to the current location of the client is determined according to the longitude and latitude coordinates included in the peripheral longitude and latitude coordinate set of the candidate recommended interest point and the longitude and latitude coordinates of the current location of the client. And if the longitude and latitude coordinates of the current position of the client are judged to be in a polygonal area formed by the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point, determining that the distance from the candidate recommended interest point to the current position of the client is 0. That is, when the distance from the candidate recommended interest point to the current location of the client is 0, the location of the client is in the area where the candidate recommended interest point is located, for example, the client is inside a building of a certain interest point. Therefore, the candidate recommended interest point can be directly determined as the current recommended interest point of the client.

Based on the same inventive concept as the method, the application also provides a device for recommending the interest points. Fig. 7 is a schematic diagram of an apparatus 2 for recommending a point of interest according to an embodiment of the present application, where the apparatus 2 includes:

a latitude and longitude coordinate obtaining unit 210, configured to respond to an interest point obtaining request from a client, and obtain a latitude and longitude coordinate of a current location of the client;

a candidate recommended interest point obtaining unit 220, configured to obtain a candidate recommended interest point of a current location of a client according to a longitude and latitude coordinate of the current location of the client and longitude and latitude coordinates of each interest point;

a distance obtaining unit 230, configured to obtain a distance from each candidate recommended interest point to a current location of the client;

an attribute information obtaining unit 240, configured to obtain attribute information of each candidate recommended interest point, where the attribute information is used to characterize the candidate recommended interest point;

a recommended interest point determining unit 250, configured to determine, from the candidate recommended interest points, a recommended interest point at the current location of the client based on a distance between each candidate recommended interest point and the current location of the client and attribute information of each candidate recommended interest point;

a returning unit 260, configured to return the recommended interest point to the client for presentation.

Optionally, the recommended point of interest determining unit 250 includes:

the candidate recommended interest point elimination determining subunit is used for determining candidate recommended interest points to be eliminated according to the distance and/or the attribute information between each candidate recommended interest point and the current position of the client;

the recommendation degree determining subunit is used for determining, for the reserved candidate recommended interest points, the recommendation degree of the candidate recommended interest points recommended to the client based on the distance from the candidate recommended interest points to the current position of the client and the attribute information;

and the selecting subunit is used for selecting the recommended interest points from the reserved candidate recommended interest points according to the determined recommendation degree.

Optionally, the attribute information includes an interest point type, an interest point weight, an interest point grade, and characteristic attribute information corresponding to the interest point type;

wherein the culling candidate recommended interest point determining subunit is configured to:

if the number of the items of the characteristic attribute information contained in the candidate recommended interest point is less than a preset number threshold, determining that the candidate recommended interest point needs to be removed;

if the distance between the candidate recommended interest point and the current position of the client is greater than the distance threshold corresponding to the type of the interest point to which the candidate recommended interest point belongs, determining that the candidate recommended interest point needs to be removed;

and if the candidate recommended interest point belongs to a preset specific type and the grade of the candidate recommended interest point is less than a preset grade threshold value, determining that the candidate recommended interest point needs to be removed.

wherein the recommendation degree determining subunit is configured to:

determining the ratio of the distance from the candidate recommended interest point to the current position of the client and the distance threshold value corresponding to the type of the interest point to which the candidate recommended interest point belongs as the normalized distance of the candidate recommended interest point;

determining the sum of the weight of the candidate recommended interest points and a preset value m as a correction weight, wherein the m is a positive number less than or equal to 1;

determining the sum of the normalized distance and a preset n value as a corrected normalized distance, wherein n is a positive number smaller than m;

determining the ratio of the correction weight to the correction normalized distance as the recommendation degree of the candidate recommended interest point recommended to the client;

a selection subunit for: and taking the candidate recommended interest point with the highest recommendation degree as a recommended interest point.

Optionally, based on any of the above embodiments, the returning unit 260 is further configured to:

and returning the characteristic attribute information of the recommended interest points to the client for display.

Optionally, based on any of the above embodiments, the distance obtaining unit 230 includes:

the peripheral longitude and latitude coordinate set acquisition subunit is used for acquiring a peripheral longitude and latitude coordinate set of the candidate recommended interest point;

the first distance acquisition subunit is used for determining the distance from the candidate recommended interest point to the current position of the client according to the longitude and latitude coordinates contained in the peripheral longitude and latitude coordinate set of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client under the condition that the peripheral longitude and latitude coordinate set can be acquired;

and the second distance acquisition subunit is used for determining the distance from the candidate recommended interest point to the current position of the client according to the longitude and latitude coordinates of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client under the condition that the peripheral longitude and latitude coordinate set cannot be acquired.

Wherein the first distance acquisition subunit is configured to:

judging whether the longitude and latitude coordinates of the current position of the client side are in a polygonal area formed by the longitude and latitude coordinates in the peripheral longitude and latitude coordinate set of the candidate recommended interest point;

if so, determining that the distance from the candidate recommended interest point to the current position of the client is 0;

if not, calculating the distance between each longitude and latitude coordinate in the peripheral longitude and latitude coordinate set and the longitude and latitude coordinate of the current position of the client, and determining the minimum distance as the distance from the candidate recommended interest point to the current position of the client.

Referring to fig. 8, optionally, the apparatus further comprises:

a determining unit 231, configured to determine whether the distance from each candidate recommended interest point to the current location of the client is 0 after the distance obtaining unit 230 obtains the distance from each candidate recommended interest point to the current location of the client, and if so, determine the candidate recommended interest point as a recommended interest point; if not, the attribute information obtaining unit 240 is triggered.

Optionally, the candidate recommended interest point obtaining unit 250 is configured to:

determining a longitude and latitude range according to the longitude and latitude coordinates of the current position of the client and a preset size;

and obtaining each interest point of the longitude and latitude coordinates in the longitude and latitude range to serve as a candidate recommended interest point of the current position of the client.

It is noted that the present application may be implemented in software and/or a combination of software and hardware, for example, the various means of the present application may be implemented using Application Specific Integrated Circuits (ASICs) or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

While exemplary embodiments have been particularly shown and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the claims.

Claims

1. A method for recommending points of interest, the method comprising the steps of:

responding to an interest point acquisition request from a client, and acquiring longitude and latitude coordinates of the current position of the client, wherein the interest point acquisition request is an acquisition request of an interest point with characteristics capable of being identified by a user;

acquiring candidate recommended interest points of the current position of the client according to the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates of each interest point;

obtaining the distance from each candidate recommended interest point to the current position of the client;

acquiring attribute information of each candidate recommended interest point for representing the characteristics of the candidate recommended interest points;

determining a recommended interest point of the current position of the client from the candidate recommended interest points based on the distance from each candidate recommended interest point to the current position of the client and the attribute information of each candidate recommended interest point, wherein the recommended interest point is an interest point with characteristics capable of being identified by a user;

and returning the recommended interest points to the client for display.

2. The method according to claim 1, wherein determining the recommended interest point of the current location of the client from the candidate recommended interest points based on the distance from each candidate recommended interest point to the current location of the client and the attribute information of each candidate recommended interest point specifically comprises:

determining candidate recommended interest points to be eliminated according to the distance and/or attribute information from each candidate recommended interest point to the current position of the client;

for the reserved candidate recommended interest points, determining the recommendation degree of the candidate recommended interest points recommended to the client based on the distance from the candidate recommended interest points to the current position of the client and the attribute information;

and selecting the recommended interest points from the reserved candidate recommended interest points according to the determined recommendation degree.

3. The method according to claim 2, wherein the attribute information includes a type of interest point, a weight of interest point, a grade of interest point, and characteristic attribute information corresponding to the type of interest point;

determining candidate recommended interest points to be eliminated according to the distance and/or attribute information from each candidate recommended interest point to the current position of the client, specifically comprising:

4. The method according to claim 2, wherein the attribute information includes a type of interest point, a weight of interest point, a grade of interest point, and characteristic attribute information corresponding to the type of interest point;

for the reserved candidate recommended interest points, determining the recommendation degree of the candidate recommended interest points to the client based on the distance from the candidate recommended interest points to the current position of the client and the attribute information, specifically comprising:

the step of selecting recommended interest points from the reserved candidate recommended interest points according to the determined recommendation degree comprises the following steps: and taking the candidate recommended interest point with the highest recommendation degree as a recommended interest point.

5. The method of any of claims 1-4, wherein the step of returning the recommended points of interest to the client for presentation further comprises:

6. The method according to any one of claims 1 to 4, wherein obtaining the distance from each candidate recommended interest point to the current location of the client specifically comprises:

acquiring a peripheral longitude and latitude coordinate set of the candidate recommended interest points;

under the condition that a peripheral longitude and latitude coordinate set can be obtained, determining the distance from a candidate recommended interest point to the current position of a client according to the longitude and latitude coordinates contained in the peripheral longitude and latitude coordinate set of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client;

and under the condition that a peripheral longitude and latitude coordinate set cannot be obtained, determining the distance from the candidate recommended interest point to the current position of the client according to the longitude and latitude coordinates of the candidate recommended interest point and the longitude and latitude coordinates of the current position of the client.

7. The method according to claim 6, wherein the determining the distance from the candidate recommended interest point to the current location of the client according to the longitude and latitude coordinates included in the peripheral longitude and latitude coordinate set of the candidate recommended interest point and the longitude and latitude coordinates of the current location of the client specifically comprises:

8. The method of claim 7, wherein after the step of obtaining the distance from each candidate recommended point of interest to the current location of the client, the method further comprises:

judging whether the distance from the candidate recommended interest point to the current position of the client is 0 or not, and if yes, determining the candidate recommended interest point as a recommended interest point;

and if not, executing the step of acquiring the attribute information of each candidate recommended interest point.

9. The method according to any one of claims 1 to 4, wherein the obtaining of the candidate recommended interest points of the current location of the client according to the longitude and latitude coordinates of the current location of the client and the longitude and latitude coordinates of each interest point specifically comprises:

10. An apparatus for recommending points of interest, the apparatus comprising:

the system comprises a latitude and longitude coordinate acquisition unit, a longitude and latitude coordinate acquisition unit and a latitude and longitude coordinate acquisition unit, wherein the latitude and longitude coordinate acquisition unit is used for responding to an interest point acquisition request from a client and acquiring the latitude and longitude coordinate of the current position of the client, and the interest point acquisition request is an interest point acquisition request with the characteristic of being capable of being identified by a user;

the candidate recommended interest point acquisition unit is used for acquiring candidate recommended interest points of the current position of the client according to the longitude and latitude coordinates of the current position of the client and the longitude and latitude coordinates of each interest point;

the distance acquisition unit is used for acquiring the distance from each candidate recommended interest point to the current position of the client;

the attribute information acquisition unit is used for acquiring the attribute information of each candidate recommended interest point for representing the characteristics of the candidate recommended interest points;

the recommendation interest point determining unit is used for determining the recommendation interest point of the current position of the client from the candidate recommendation interest points on the basis of the distance from each candidate recommendation interest point to the current position of the client and the attribute information of each candidate recommendation interest point, wherein the recommendation interest points are the interest points with characteristics capable of being identified by a user;

and the return unit is used for returning the recommended interest points to the client for display.

11. The apparatus of claim 10, wherein the recommended point of interest determining unit comprises:

12. The apparatus according to claim 11, wherein the attribute information includes a point of interest type, a point of interest weight, a point of interest level, and characteristic attribute information corresponding to the point of interest type;

the candidate recommended interest point elimination determination subunit is configured to:

13. The apparatus according to claim 11, wherein the attribute information includes a point of interest type, a point of interest weight, a point of interest level, and characteristic attribute information corresponding to the point of interest type;

the recommendation degree determination subunit is configured to:

14. The apparatus according to any one of claims 10-13, wherein the return unit is further configured to:

15. The apparatus according to any one of claims 10 to 13, wherein the distance acquisition unit comprises:

16. The apparatus of claim 15, wherein the first distance obtaining subunit is configured to:

17. The apparatus of claim 16, further comprising:

the judging unit is used for judging whether the distance from each candidate recommended interest point to the current position of the client is 0 or not after the distance obtaining unit obtains the distance from each candidate recommended interest point to the current position of the client, and if yes, determining the candidate recommended interest points as the recommended interest points; if not, the attribute information acquisition unit is triggered.

18. The apparatus according to any of claims 10-13, wherein the candidate recommended point of interest obtaining unit is configured to: