CN114357293B - Object recommendation method, device, electronic device and computer-readable storage medium - Google Patents
Object recommendation method, device, electronic device and computer-readable storage mediumInfo
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- CN114357293B CN114357293B CN202111652023.7A CN202111652023A CN114357293B CN 114357293 B CN114357293 B CN 114357293B CN 202111652023 A CN202111652023 A CN 202111652023A CN 114357293 B CN114357293 B CN 114357293B
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Abstract
本申请提供了一种对象推荐方法、装置、电子设备及计算机可读存储介质；方法包括：获得待推荐对象的属性信息及多个参考对象中每个参考对象的属性信息；基于待推荐对象的属性信息及多个参考对象中每个参考对象的属性信息，从多个参考对象中确定与待推荐对象具有属性关联的目标参考对象；获得目标用户针对目标参考对象的评分信息；基于评分信息、待推荐对象的属性信息及目标参考对象的属性信息，对目标用户针对待推荐对象的评分进行预测，得到相应的第一预测评分；基于第一预测评分，确定待推荐对象是否满足推荐条件；当待推荐对象满足推荐条件时，将待推荐对象推荐给目标用户。通过本申请，能够使推荐的对象更符合用户的喜好。
The present application provides an object recommendation method, apparatus, electronic device, and computer-readable storage medium. The method includes: obtaining attribute information of an object to be recommended and attribute information of each reference object among a plurality of reference objects; determining a target reference object having attribute associations with the object to be recommended from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object among the plurality of reference objects; obtaining rating information of the target reference object by a target user; predicting the target user's rating of the object to be recommended based on the rating information, the attribute information of the object to be recommended, and the attribute information of the target reference object to obtain a corresponding first predicted rating; determining whether the object to be recommended meets recommendation conditions based on the first predicted rating; and recommending the object to be recommended to the target user when the object to be recommended meets the recommendation conditions. Through the present application, the recommended objects can be made more in line with the user's preferences.
Description
Technical Field
The present application relates to computer technologies, and in particular, to an object recommendation method, an object recommendation device, an electronic device, and a computer readable storage medium.
Background
In the current recommendation method for the object, evaluation information of the object by the user is needed to be more or less to recommend based on average information of the user, however, the problem of data sparseness in a recommendation algorithm is solved by the method, the recommendation of the completely new online object is difficult to realize, and the online object cannot be well matched with user preference by using user evaluation.
Disclosure of Invention
The embodiment of the application provides an object recommending method, an object recommending device, electronic equipment and a computer readable storage medium, which can enable recommended objects to be more in line with the preference of users.
The technical scheme of the embodiment of the application is realized as follows:
the embodiment of the application provides an object recommendation method, which comprises the following steps:
Acquiring attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
Determining a target reference object which is related to the object to be recommended and has the attribute from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
obtaining grading information of a target user aiming at the target reference object;
Predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
Determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
And recommending the object to be recommended to the target user when the object to be recommended meets the recommendation condition.
In the scheme, the step of predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object comprises the steps of determining the number of attributes of the target reference object based on the attribute information of the target reference object, determining the number of identical attributes between the target reference object and the object to be recommended, and predicting the score of the target user for the object to be recommended based on the score information, the number of attributes of the target reference object and the number of identical attributes.
In the scheme, the attribute information of the reference object comprises release time of the reference object, and the determining whether the object to be recommended meets the recommendation condition based on the first prediction score comprises obtaining the scoring time of the target user for the target reference object when the scoring information of the user does not exist in the object to be recommended, determining the second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object, and determining whether the object to be recommended meets the recommendation condition based on the first prediction score and the second prediction score.
In the above scheme, the number of the target reference objects is a plurality, and the determining the second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference objects includes determining time intervals between the scoring time of the target user for each target reference object and the release time of the corresponding target reference object, summing the time intervals corresponding to the plurality of target reference objects to obtain a sum of the time intervals, determining the number of the target reference objects, and taking the sum of the number of the target reference objects and the time intervals as the second prediction score.
In the scheme, whether the object to be recommended meets the recommendation condition or not is determined based on the first prediction score and the second prediction score, the method comprises the steps of determining the sum of scores of the first prediction score and the second prediction score, determining that the object to be recommended meets the recommendation condition when the sum of scores is larger than or equal to a score threshold value, and determining that the object to be recommended does not meet the recommendation condition when the sum of scores is smaller than the score threshold value.
In the scheme, whether the object to be recommended meets the recommendation condition or not is determined based on the first prediction scores, the method comprises the steps of obtaining evaluation reference objects of evaluation of the target user when the object to be recommended has scoring information of users, obtaining the number of users of a plurality of other users evaluating the evaluation reference objects and the number of reference objects of evaluation of each other user in the plurality of other users, wherein the plurality of other users do not comprise the target user, determining third prediction scores of the target user for the object to be recommended based on the number of users of the plurality of other users and the number of reference objects of evaluation of each other user, and determining whether the object to be recommended meets the recommendation condition or not based on the first prediction scores and the third prediction scores.
In the scheme, whether the object to be recommended meets the recommendation condition or not is determined based on the first prediction score and the third prediction score, the method comprises the steps of obtaining a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score, carrying out weighted summation on the first prediction score and the third prediction score based on the first weight and the second weight to obtain a corresponding target prediction score, and determining whether the object to be recommended meets the recommendation condition or not based on the target prediction score.
The embodiment of the application provides an object recommendation device, which comprises:
the first obtaining module is used for obtaining attribute information of an object to be recommended and attribute information of each reference object in the plurality of reference objects;
the target reference object determining module is used for determining a target reference object which is related to the object to be recommended and has an attribute from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
The second obtaining module is used for obtaining grading information of the target user aiming at the target reference object;
The scoring prediction module is used for predicting the score of the target user aiming at the object to be recommended based on the scoring information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
The determining module is used for determining whether the object to be recommended meets a recommendation condition or not based on the first prediction score;
and the recommending module is used for recommending the object to be recommended to the target user when the object to be recommended meets the recommending condition.
An embodiment of the present application provides an electronic device, including:
a memory for storing executable instructions;
And the processor is used for realizing the object recommendation method provided by the embodiment of the application when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium which stores executable instructions for realizing the object recommendation method provided by the embodiment of the application when being executed by a processor.
The embodiment of the application has the following beneficial effects:
The method comprises the steps of obtaining attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects, determining a target reference object which is related to the object to be recommended and has an attribute from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects, obtaining grading information of a target user aiming at the target reference object, predicting the grading of the target user aiming at the object to be recommended based on the grading information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction grading, determining whether the object to be recommended meets the recommendation condition based on the first prediction grading, and recommending the object to be recommended to the target user when the object to be recommended meets the recommendation condition, so that the recommended object can better meet the preference of the user.
Drawings
FIG. 1 is a schematic diagram of an alternative architecture of an object recommendation system architecture according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative architecture of an electronic device 200 according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of an alternative object recommendation method according to an embodiment of the present application;
FIG. 4 is a schematic illustration of an alternative refinement of step 304 provided by an embodiment of the present application;
FIG. 5 is a schematic illustration of an alternative refinement of step 305 provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of an alternative refinement of step 305 provided by an embodiment of the present application.
Detailed Description
The present application will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a specific ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a specific order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
Based on the above, the embodiment of the application provides an object recommending method, an object recommending device, electronic equipment and a computer readable storage medium, which can enable recommended objects to be more in line with user preference.
Referring to fig. 1, fig. 1 is a schematic diagram of an alternative architecture of an object recommendation system 100 according to an embodiment of the present application, where a terminal 103 is connected to a server 101 through a network 102. In some embodiments, terminal 103 may be, but is not limited to, a notebook computer, tablet computer, desktop computer, smart phone, dedicated messaging device, portable gaming device, smart speaker, smart watch, etc. The server 101 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery network (CDN, content Delivery Network) services, basic cloud computing services such as big data and an artificial intelligence platform. The network 102 may be a wide area network or a local area network, or a combination of both. The terminal 103 and the server 101 may be directly or indirectly connected through wired or wireless communication, which is not limited in the embodiment of the present application.
Next, referring to fig. 2, fig. 2 is a schematic structural diagram of an alternative electronic device 200 provided in the embodiment of the present application, where in practical application, the electronic device 200 may be implemented as the terminal 103 or the server 101 in fig. 1, and the electronic device is taken as the terminal 103 shown in fig. 1 as an example, and an electronic device implementing the object recommendation method in the embodiment of the present application is described. The electronic device 200 shown in fig. 2 comprises at least one processor 201, a memory 205, at least one network interface 202 and a user interface 203. The various components in the electronic device 200 are coupled together by a bus system 204. It is understood that the bus system 204 is used to enable connected communications between these components. The bus system 204 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 204 in fig. 2.
The Processor 201 may be an integrated circuit chip with signal processing capabilities such as a general purpose Processor, such as a microprocessor or any conventional Processor, a digital signal Processor (DSP, digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
The user interface 203 includes one or more output devices 2031, including one or more speakers and/or one or more visual displays, that enable presentation of media content. The user interface 203 also includes one or more input devices 2032 including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.
The memory 205 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid state memory, hard drives, optical drives, and the like. Memory 205 may optionally include one or more storage devices physically located remote from processor 201.
Memory 205 includes volatile memory or nonvolatile memory, and may also include both volatile and nonvolatile memory. The non-volatile Memory may be a Read Only Memory (ROM) and the volatile Memory may be a random access Memory (RAM, random Access Memory). The memory 205 described in embodiments of the present application is intended to comprise any suitable type of memory.
In some embodiments, the memory 205 is capable of storing data, examples of which include programs, modules and data structures, or subsets or supersets thereof, to support various operations, and in embodiments of the present application the memory 205 stores an operating system 2051, a network communication module 2052, a rendering module 2053, an input processing module 2054, and an object recommendation device 2055, and in particular,
The operating system 2051, which includes system programs for handling various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and handling hardware-based tasks;
The network communication module 2052 for accessing other computing devices via one or more (wired or wireless) network interfaces 202, exemplary network interfaces 202 include bluetooth, wireless compatibility authentication (WiFi), and universal serial bus (USB, universal Serial Bus), among others;
A presentation module 2053 for enabling presentation of information (e.g., user interfaces for operating peripheral devices and displaying content and information) via one or more output devices 2031 (e.g., a display screen, speakers, etc.) associated with the user interface 203;
the input processing module 2054 is configured to detect one or more user inputs or interactions from one of the one or more input devices 2032 and translate the detected inputs or interactions.
In some embodiments, the object recommending apparatus provided by the embodiments of the present application may be implemented in software, and fig. 2 shows the object recommending apparatus 2055 stored in the memory 205, which may be software in the form of a program, a plug-in, or the like, including software modules including a first obtaining module 20551, a target reference object determining module 20552, a second obtaining module 20553, a scoring prediction module 20554, a determining module 20555, and a recommending module 20556, which are logical, and thus may be arbitrarily combined or further split according to the implemented functions. The functions of the respective modules will be described hereinafter.
In other embodiments, the object recommendation apparatus provided in the embodiments of the present application may be implemented in hardware, and by way of example, the object recommendation apparatus provided in the embodiments of the present application may be a processor in the form of a hardware decoding processor that is programmed to perform the object recommendation method provided in the embodiments of the present application, for example, the processor in the form of a hardware decoding processor may employ one or more Application Specific Integrated Circuits (ASICs), DSPs, programmable logic devices (PLDs, programmable Logic Device), complex Programmable logic devices (CPLDs, complex Programmable Logic Device), field Programmable Gate Arrays (FPGAs), field-Programmable GATE ARRAY), or other electronic components.
The object recommendation method provided by the embodiment of the application will be described in connection with the exemplary application and implementation of the terminal provided by the embodiment of the application.
Referring to fig. 3, fig. 3 is a schematic flowchart of an alternative method for recommending objects according to an embodiment of the present application, and will be described with reference to the steps shown in fig. 3.
Step 301, obtaining attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
step 302, determining a target reference object associated with the attribute of the object to be recommended from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
Step 303, obtaining grading information of a target user aiming at the target reference object;
step 304, predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object, so as to obtain a corresponding first prediction score;
step 305, determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
And step 306, recommending the object to be recommended to the target user when the object to be recommended meets the recommendation condition.
In a practical scenario, the object to be recommended and the reference object according to the embodiment of the present application are the same type of object, for example, but not limited to, video, music, merchandise, and the like. Wherein the video may be, but is not limited to, a television show or a movie. The attribute information of the object includes a plurality of attributes, which may be, but are not limited to, the name of the object, release time, place of origin information (e.g., country of production or production), object classification, etc. For example, when the object is a movie item, the attribute information of the object may include, but is not limited to, movie name, release year (i.e., release time), subject matter (i.e., object classification), country (and information of the place of origin of the object), time duration, and the like.
Here, the attribute information of the object to be recommended and the attribute information of the plurality of reference objects may be stored in the server, or may be stored in an external storage device, such as a database, etc., which is communicatively connected to the server. In actual implementation, the server acquires attribute information of an object to be recommended and attribute information of each reference object in the plurality of reference objects from local or external storage equipment. Then, a target reference object associated with the object to be recommended is determined from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects. Here, having an attribute association with an object to be recommended means having at least one same attribute as the object to be recommended. In some embodiments, having attribute associations with the object to be recommended may also mean that the attribute information has similarity with the attribute information of the object to be recommended.
In the embodiment of the application, the server compares the attribute information of the object to be recommended with the attribute information of a plurality of reference objects respectively to obtain a target reference object which has attribute association with the object to be recommended in the plurality of reference objects. Here, the number of target reference objects is at least two.
Then, the server obtains scoring information of at least two target users for the target reference object. Here, the scoring information includes a scoring value of the target user for the target reference object. It should be appreciated that when a target user does not score a certain target reference object, the server cannot obtain the scoring information of the target reference object. In actual implementation, after obtaining the scoring information of the target reference object, the server predicts the scoring of the target user for the object to be recommended based on the scoring information of each target reference object, the attribute information of the object to be recommended and the attribute information of the target reference object, and obtains a corresponding first prediction score.
Specifically, referring to fig. 4, fig. 4 is an optional refinement flowchart of step 304 provided in an embodiment of the present application, where step 304 may be further implemented by:
Step 401, determining the number of attributes of the target reference object based on the attribute information of the target reference object;
step 402, determining the number of the same attributes between the target reference object and the object to be recommended;
And step 403, predicting the score of the target user for the object to be recommended based on the score information, the number of the attributes of the target reference object and the number of the same attributes.
In actual implementation, the server counts the number of attributes of the corresponding target reference object based on the attribute information of each target reference object. And then, comparing attribute information of the target reference object and the object to be recommended, determining the same attribute of the target reference object and the object to be recommended, and counting the number of the same attribute. The server determines a first prediction score of the target user for the object to be recommended based on the scoring information, the number of attributes of the target reference object, and the number of the same attributes. In one embodiment, the server determines a first predictive score f a (u) for the target user u for the object to be recommended by equation (1):
wherein AC j represents the number of attributes possessed by the target reference object j, IAC l represents the number of target reference objects having an attribute l in common with the object to be recommended, r uj =1 if the target reference object j is evaluated by the target user u, r uj =0 if the target reference object j has an attribute l, h lj =1 if the target reference object j has an attribute l, h lj = 0;p if the target reference object j has an attribute l, and n is the number of target reference objects.
Based on the above, the association between the object to be recommended and the plurality of target reference objects is determined according to the scoring information of the target user on each target reference object, the attribute information of the object to be recommended, the attribute information of each target reference object, the number of the attributes of each target reference object, and the number of target reference objects having common attributes with the object to be recommended, so as to obtain a first prediction score of the target user on the object to be recommended.
Next, the server determines whether the object to be recommended satisfies the recommendation condition based on the first prediction score. Here, the server may determine whether the object to be recommended satisfies the recommendation condition by determining whether the first prediction score reaches the first score threshold. And when the first predictive score reaches the first score threshold, determining that the object to be recommended meets the recommendation condition, and when the first predictive score does not reach the first score threshold, determining that the object to be recommended does not meet the recommendation condition.
In some embodiments, the attribute information of the reference object includes a publication time of the reference object. Referring to fig. 5, fig. 5 is a schematic flowchart of an optional refinement of step 305 provided in an embodiment of the present application, where step 305 may be further implemented as follows:
Step 501, when the object to be recommended does not have the scoring information of the user, obtaining the scoring time of the target user for the target reference object;
step 502, determining a second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object;
Step 503, determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the second prediction score.
In an actual scene, an object to be recommended to a target user may be a newly issued object, and no scoring information of any user exists. In the embodiment of the application, whether the object to be recommended meets the recommendation condition is further determined by combining the scoring time of the target user for the target reference and the first prediction scoring. Specifically, the server obtains a scoring time of the target user for the target reference object, and determines a second prediction score of the target user for the object to be recommended based on the scoring time and the release time of the target reference object.
Here, the number of target reference objects is a plurality. In some embodiments, step 502 may further be implemented by determining a time interval between the scoring time of the target user for each target reference object and the release time of the corresponding target reference object, summing the time intervals corresponding to the plurality of target reference objects to obtain a sum of the time intervals, determining the number of target reference objects, and taking the sum of the number of target reference objects and the time intervals as the second prediction score.
Specifically, the server may determine the second prediction score of the target user u for the object to be recommended through formula (2):
Wherein, the smaller the value of (time ui-datei), the larger w u indicates that the closer the time the user evaluates the item and the time the item is released, indicating that the user is more aggressive and likes to pay attention to new things, whereas the larger the value of (time ui-datei), the smaller w u indicates that the user is more negative and likes to pay attention to items that have been focused or evaluated by many users. Equation (2) reflects the average degree of user preference evaluation of the newly released object. During the process of recommending new items, both active and passive users may be present, but the preference is to recommend new items to active users.
In some embodiments, step 503 may also be implemented by determining a sum of scores of the first prediction score and the second prediction score, determining that the object to be recommended satisfies the recommendation condition when the sum of scores is greater than or equal to a score threshold, and determining that the object to be recommended does not satisfy the recommendation condition when the sum of scores is less than the score threshold.
In actual implementation, the server sums the first prediction score and the second prediction score to obtain a score sumBased on the obtained score sumTo determine whether the object to be recommended satisfies the recommendation condition.
In some embodiments, referring to fig. 6, fig. 6 is an optional refinement flowchart of step 305 provided by an embodiment of the present application, where step 305 may be further implemented by:
Step 601, when the object to be recommended has the grading information of the user, obtaining an evaluation reference object of the target user evaluation;
Step 602, obtaining the number of users of a plurality of other users evaluating the evaluation reference object and the number of reference objects evaluated by each other user in the plurality of other users, wherein the plurality of other users do not comprise the target user;
Step 603, determining a third prediction score of the target user for the object to be recommended based on the number of users of the plurality of other users and the number of reference objects evaluated by each other user;
Step 604, determining whether the object to be recommended meets a recommendation condition based on the first prediction score and the third prediction score.
When the target to be recommended has the scoring information of the user, the scoring prediction of the target user is carried out on the target to be recommended according to the scoring information of the target user on the target reference object, the corresponding third prediction score is obtained, and whether the target to be recommended meets the recommendation condition or not is determined according to the first prediction score and the third prediction score. Specifically, the server may determine a third predictive score for the target user u for the object to be recommended by formula (3):
Where f u (u) represents the predictive score value of user u for target reference object j. If user u evaluates item j, then r uj =1, and conversely, r uj =0. If user k evaluates item j, then r kj =1, whereas r kj＝0.ICk represents the number of items evaluated by user k, and UC j represents the number of users commonly evaluating item j. Then, based on the scoring information of the user for the items and the number of the user-evaluated items and the number of times the items are evaluated by the user, a predicted scoring value based on the scoring information of the items may be defined so as to mine the relationship between them.
In some embodiments, the step 604 may further be implemented by obtaining a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score, performing weighted summation on the first prediction score and the third prediction score based on the first weight and the second weight to obtain a corresponding target prediction score, and determining whether the object to be recommended satisfies a recommendation condition based on the target prediction score.
Specifically, the server may calculate a weighted sum between the first predictive score and the third predictive score by equation (4):
Wherein UC j represents the number of users of the common evaluation item j, and w u represents the time weight of the user u. If item j is a new item, then UC j =0, whereas if otherwise, UC j +.0. The user time weight information refers to the time interval between the user scoring time and the project release time, has no relation with whether the project is a new project or not, but can directly reflect the preference degree of the user on the project from another angle.
According to the embodiment of the application, the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects are obtained, the target reference object which is related to the object to be recommended and has the attribute is determined from the plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects, the grading information of the target user aiming at the target reference object is obtained, the grading of the target user aiming at the object to be recommended is predicted based on the grading information, the attribute information of the object to be recommended and the attribute information of the target reference object, the corresponding first prediction grading is obtained, whether the object to be recommended meets the recommendation condition is determined based on the first prediction grading, and when the object to be recommended meets the recommendation condition, the object to be recommended is recommended to the target user, so that the recommended object better meets the preference of the user.
Continuing with the description below of an exemplary architecture of the object recommendation device 555 implemented as a software module provided by embodiments of the present application, in some embodiments, as shown in fig. 2, the software module stored in the object recommendation device 20551 of the memory 2055 may include:
the first obtaining module is used for obtaining attribute information of an object to be recommended and attribute information of each reference object in the plurality of reference objects;
the target reference object determining module is used for determining a target reference object which is related to the object to be recommended and has an attribute from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
The second obtaining module is used for obtaining grading information of the target user aiming at the target reference object;
The scoring prediction module is used for predicting the score of the target user aiming at the object to be recommended based on the scoring information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
The determining module is used for determining whether the object to be recommended meets a recommendation condition or not based on the first prediction score;
and the recommending module is used for recommending the object to be recommended to the target user when the object to be recommended meets the recommending condition.
In some embodiments, the scoring prediction module is further configured to determine a number of attributes that the target reference object has based on the attribute information of the target reference object, determine a number of identical attributes that the target reference object has with the object to be recommended, and predict the scoring of the target user for the object to be recommended based on the scoring information, the number of attributes that the target reference object has, and the number of identical attributes.
In some embodiments, the attribute information of the reference object includes a release time of the reference object, and the determining module is further configured to obtain a score time of the target user for the target reference object when the target object to be recommended does not have score information of a user, determine a second prediction score of the target user for the target object to be recommended based on the score time and the release time of the target reference object, and determine whether the target object to be recommended satisfies a recommendation condition based on the first prediction score and the second prediction score.
In some embodiments, the number of the target reference objects is multiple, and the scoring prediction module is further configured to determine a time interval between a scoring time of the target user for each target reference object and a release time of the corresponding target reference object, sum the time intervals corresponding to the multiple target reference objects to obtain a sum of the time intervals, determine the number of the target reference objects, and use the sum of the number of the target reference objects and the time intervals as the second prediction score.
In some embodiments, the determining module is further configured to determine a sum of scores of the first prediction score and the second prediction score, determine that the object to be recommended satisfies a recommendation condition when the sum of scores is greater than or equal to a score threshold, and determine that the object to be recommended does not satisfy the recommendation condition when the sum of scores is less than the score threshold.
In some embodiments, the determining module is further configured to obtain a rating reference object for the target user rating when the rating information of the user exists for the object to be recommended, obtain a number of users of a plurality of other users that rate the rating reference object and a number of reference objects for each of the plurality of other users that do not include the target user, determine a third prediction score for the target user for the object to be recommended based on the number of users of the plurality of other users and the number of reference objects for each of the other users to be recommended, and determine whether the object to be recommended meets a recommendation condition based on the first prediction score and the third prediction score.
In some embodiments, the determining module is further configured to obtain a first weight corresponding to the first prediction score and a second weight corresponding to the third prediction score, weight and sum the first prediction score and the third prediction score based on the first weight and the second weight to obtain a corresponding target prediction score, and determine whether the object to be recommended meets a recommendation condition based on the target prediction score.
Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the object recommendation method according to the embodiment of the present application.
Embodiments of the present application provide a computer readable storage medium having stored therein executable instructions that, when executed by a processor, cause the processor to perform the method provided by the embodiments of the present application.
In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM, or various devices including one or any combination of the above.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, such as in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
As an example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices located at one site or distributed across multiple sites and interconnected by a communication network.
In summary, through the embodiment of the application, the object recommended to the user can be more in line with the preference of the user.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and scope of the present application are included in the protection scope of the present application.
Claims (8)
1. An object recommendation method, comprising:
Acquiring attribute information of an object to be recommended and attribute information of each reference object in a plurality of reference objects;
Determining a target reference object which is related to the object to be recommended and has the attribute from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
obtaining grading information of a target user aiming at the target reference object;
Predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
Determining whether the object to be recommended meets a recommendation condition based on the first prediction score;
when the object to be recommended meets the recommendation condition, recommending the object to be recommended to the target user;
Wherein the attribute information of the reference object includes a release time of the reference object, and the determining, based on the first prediction score, whether the object to be recommended meets a recommendation condition includes:
When the object to be recommended does not have the scoring information of the user, the scoring time of the target user for the target reference objects is obtained, and the number of the target reference objects is a plurality of;
Respectively determining a time interval between the scoring time of the target user for each target reference object and the release time of the corresponding target reference object;
Summing the time intervals corresponding to the plurality of target reference objects to obtain the sum of the time intervals;
determining the number of the target reference objects, and taking the ratio of the number of the target reference objects to the sum of the time intervals as a second prediction score;
And determining whether the object to be recommended meets a recommendation condition or not based on the first prediction score and the second prediction score.
2. The object recommendation method according to claim 1, wherein predicting the score of the target user for the object to be recommended based on the score information, the attribute information of the object to be recommended, and the attribute information of the target reference object, comprises:
Determining the attribute quantity of the target reference object based on the attribute information of the target reference object;
Determining the number of the same attributes between the target reference object and the object to be recommended;
And predicting the score of the target user for the object to be recommended based on the score information, the number of the attributes of the target reference object and the number of the same attributes.
3. The method of claim 1, wherein determining whether the object to be recommended satisfies a recommendation condition based on the first prediction score and the second prediction score comprises:
determining a sum of scores of the first predictive score and the second predictive score;
when the scoring sum is greater than or equal to a scoring threshold value, determining that the object to be recommended meets a recommendation condition;
And when the scoring sum is smaller than a scoring threshold value, determining that the object to be recommended does not meet the recommendation condition.
4. The method of claim 1, wherein determining whether the object to be recommended satisfies a recommendation condition based on the first predictive score comprises:
When the object to be recommended has the grading information of the user, acquiring an evaluation reference object of the target user evaluation;
Obtaining the number of users of a plurality of other users evaluating the evaluation reference object and the number of reference objects evaluated by each other user in the plurality of other users, wherein the plurality of other users do not comprise the target user;
determining a third prediction score of the target user for the object to be recommended based on the number of users of the plurality of other users and the number of reference objects evaluated by each other user;
based on the first predictive score and the third predictive score, it is determined whether the object to be recommended satisfies a recommendation condition.
5. The method of claim 4, wherein determining whether the object to be recommended satisfies a recommendation condition based on the first predictive score and the third predictive score comprises:
obtaining a first weight corresponding to the first predictive score and a second weight corresponding to the third predictive score;
Based on the first weight and the second weight, carrying out weighted summation on the first prediction score and the third prediction score to obtain a corresponding target prediction score;
And determining whether the object to be recommended meets a recommendation condition or not based on the target prediction score.
6. An object recommendation device, characterized by comprising:
the first obtaining module is used for obtaining attribute information of an object to be recommended and attribute information of each reference object in the plurality of reference objects;
the target reference object determining module is used for determining a target reference object which is related to the object to be recommended and has an attribute from a plurality of reference objects based on the attribute information of the object to be recommended and the attribute information of each reference object in the plurality of reference objects;
The second obtaining module is used for obtaining grading information of the target user aiming at the target reference object;
The scoring prediction module is used for predicting the score of the target user aiming at the object to be recommended based on the scoring information, the attribute information of the object to be recommended and the attribute information of the target reference object to obtain a corresponding first prediction score;
The determining module is used for determining whether the object to be recommended meets a recommendation condition or not based on the first prediction score;
the recommending module is used for recommending the object to be recommended to the target user when the object to be recommended meets the recommending condition;
The determining module is further configured to obtain, when the object to be recommended does not have scoring information of a user, scoring time of the target user for the target reference object, where the number of the target reference objects is multiple;
Respectively determining a time interval between the scoring time of the target user for each target reference object and the release time of the corresponding target reference object;
Summing the time intervals corresponding to the plurality of target reference objects to obtain the sum of the time intervals;
determining the number of the target reference objects, and taking the ratio of the number of the target reference objects to the sum of the time intervals as a second prediction score;
And determining whether the object to be recommended meets a recommendation condition or not based on the first prediction score and the second prediction score.
7. An electronic device, comprising:
a memory for storing executable instructions;
A processor for implementing the object recommendation method of any one of claims 1 to 5 when executing executable instructions stored in the memory.
8. A computer readable storage medium storing executable instructions for implementing the object recommendation method according to any one of claims 1 to 5 when executed by a processor.
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| CN107729542B (en) * | 2017-10-31 | 2021-05-04 | 咪咕音乐有限公司 | A kind of information scoring method and device and storage medium |
| CN110209938B (en) * | 2019-05-31 | 2022-10-25 | 河南大学 | Potential user recommendation method based on service multi-granularity attribute |
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| CN109684538A (en) * | 2018-12-03 | 2019-04-26 | 重庆邮电大学 | A kind of recommended method and recommender system based on individual subscriber feature |
| CN113254773A (en) * | 2021-06-02 | 2021-08-13 | 南京邮电大学 | New project recommendation method based on predictive value analysis |
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