CN111177562A

CN111177562A - Recommendation and sorting processing method and device for target objects and server

Info

Publication number: CN111177562A
Application number: CN201911423573.4A
Authority: CN
Inventors: 冯欢
Original assignee: Bank of China Ltd
Current assignee: Bank of China Ltd
Priority date: 2019-12-31
Filing date: 2019-12-31
Publication date: 2020-05-19
Anticipated expiration: 2039-12-31
Also published as: CN111177562B

Abstract

The embodiment of the specification discloses a recommendation and ranking processing method and device for target objects and a server. In some embodiments, the influence of the increment frequency and the attenuation frequency on the sorting result is comprehensively considered, and the influence degree of increment and attenuation is also considered. Determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length; and determining a recommendation sequencing result of the target object according to the priority weight. In some embodiments of the description, an increasing frequency parameter and an attenuating frequency parameter can be set, each parameter can be configured with an individual step length, so that a priority weight score is calculated, a simple and efficient method for priority ranking recommendation can be quickly obtained, complicated processes such as big data acquisition and machine learning and costs associated with the processes are avoided, and meanwhile, the flexibility of adapting to different scenes is retained.

Description

Recommendation and sorting processing method and device for target objects and server

Technical Field

The embodiment scheme of the specification belongs to the technical field of computer data processing in financial services, and particularly relates to a recommendation and ranking processing method and device for target objects and a server.

Background

There are many scenarios in software application development that require a recommendation ranking. For example, when an application generates a plurality of push messages of different services in a certain period of time, if the messages are all pushed to a user at the same time, poor application experience is caused, and therefore, the messages of the services are often required to be sorted, and the message with the top rank is selected as the message preferentially recommended to the user. Of course, there are other pushing requirements in different scenarios, such as experience of new products, issuance of coupons, and the like, and in application development or design, optimization problems of recommendation ranking often need to be faced.

The existing development recommendation mechanism is generally completed by means of machine learning and large data acquisition and analysis. The process flow of the existing scheme generally includes:

a) collecting a large amount of customer behavior data, filtering and cleaning the data, selecting key data factors, and dividing the key data factors into a training set and a testing set;

b) preprocessing the behavior data of the clients, and performing supervised or unsupervised learning on the clients to classify the clients into clusters;

c) selecting a multi-dimensional machine learning algorithm, and learning a recommendation model based on customer grouping and behavior data;

d) and performing iterative tuning on the recommendation model to determine the recommendation model. And recommending by using the sequencing result output by the recommendation model.

The existing scheme needs a large amount of training data to perform machine learning, the model training process is complicated, the time consumption is long, the resource consumption of a machine learning computer is high, and the acquisition cost of a large amount of data is high. Finally, the generated recommendation model is poor in thriving readability and understandability, the prediction result completely depends on the training sample, and the reliability of the output result is difficult to control.

Therefore, there is a need in the art for a recommendation implementation scheme that can be more concise, reliable, and flexibly configured to meet different scenario requirements.

Disclosure of Invention

Embodiments of the present disclosure provide a method, an apparatus, and a server for processing recommendation ranking of target objects, which can quickly complete establishment of a recommendation mechanism, obtain a recommendation ranking result, reduce complexity of obtaining recommendation ranking, and reduce cost and resource consumption.

The recommendation and ranking processing method, device and server for the target object provided by the embodiments of the present specification are implemented in the following manners:

a recommendation ranking processing method for a target object, the method comprising:

acquiring ascending frequency data and attenuation frequency data of a target object, wherein the ascending frequency data comprises a factor which is determined to be capable of increasing the sequencing priority, and the attenuation frequency data comprises a factor which is determined to be capable of reducing the sequencing priority;

respectively determining an increment step length corresponding to the increment frequency data and an attenuation step length corresponding to the attenuation frequency data, wherein the increment step length represents influence degree data of factors on increasing the sequencing priority, and the attenuation step length represents influence degree data of factors on reducing the sequencing priority;

determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length;

and determining a recommendation sequencing result of the target object according to the priority weight.

In one embodiment of the method, the incremental frequency data includes a number of times the target object is acted upon by the user.

In one method embodiment, the decay frequency data includes a length of time that the target object is not acted upon by the user.

In an embodiment of the method, the determining the priority weight of the target object according to the increment frequency data, the increment step size, the attenuation frequency data, and the attenuation step size includes:

calculating the fluctuation range of the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length;

and adding the fluctuation amplitude and the priority weight of the target object last time to obtain the priority weight of the current sequence of the target object.

In one embodiment of the method, the fluctuation range of the priority weight is calculated by the following method:

the fluctuation range is (increment frequency data 1 × increment step 1+ increment frequency data 2 × increment step 2+ … + increment frequency data M × increment step M) - (attenuation frequency data 1 × attenuation step 1+ attenuation frequency data 2 × attenuation step 2+ … + attenuation frequency data M × attenuation step N), where M is the number of selected increment frequency data, and N is the number of attenuation frequency data;

correspondingly, the priority weight of the current ordering of the target object is:

the priority weight is equal to the original priority weight plus the fluctuation range.

In one embodiment of the method, the increasing frequency data comprises the number of times of use of the target object, and the decreasing frequency data comprises the number of days of time that the target object is not used by the user;

the fluctuation amplitude of the priority weight at least comprises fluctuation data obtained by adopting the following modes:

number of uses increment step-days of time decay step.

In one method embodiment, the increment frequency data further comprises: user job level of the user;

number of uses increment step + user step increment step-days of time decay step.

In one embodiment of the method, the target objects are different service items in an application;

or,

the target objects are different applications in the terminal.

A recommendation ranking processing apparatus for a target object, the apparatus comprising:

the system comprises a sequencing factor module, a data processing module and a data processing module, wherein the sequencing factor module is used for acquiring ascending frequency data and attenuation frequency data of a target object, the ascending frequency data comprises a factor which is determined to be capable of increasing sequencing priority, and the attenuation frequency data comprises a factor which is determined to be capable of reducing sequencing priority;

a step length determining module, configured to determine an increment step length corresponding to the increment frequency data and an attenuation step length corresponding to the attenuation frequency data, respectively, where the increment step length represents influence degree data of a factor on increasing the ranking priority, and the attenuation step length represents influence degree data of a factor on decreasing the ranking priority;

the weight calculation module is used for determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length;

and the recommendation calculation module is used for determining a recommendation sequencing result of the target object according to the priority weight.

In one embodiment, the incremental frequency data includes a number of times the target object is acted upon by the user.

In one embodiment, the decay frequency data includes a length of time that the target object is not acted upon by the user.

In an embodiment of the apparatus, the determining the priority weight of the target object according to the increment frequency data, the increment step size, the decay frequency data, and the decay step size includes:

In one embodiment of the apparatus, the fluctuation range of the priority weight is calculated by:

In one embodiment of the apparatus, the increasing frequency data comprises the number of times the target object is used, and the decreasing frequency data comprises the number of days of time the target object is not used by the user;

number of uses increment step-days of time decay step.

In one embodiment of the apparatus, the increment frequency data further comprises: user job level of the user;

In one embodiment of the apparatus, the target objects are different service items in an application;

or,

the target objects are different applications in the terminal.

A recommendation server for a target object, comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implement:

The method, the device and the server for processing recommendation and ranking of the target object provided in the embodiments of the present specification can comprehensively consider the influence of the increasing frequency (for example, the number of times of use) and the attenuating frequency (for example, time lapse) on the ranking result, and also consider the degree of influence of the increasing frequency and the attenuating frequency. Determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length; and determining a recommendation sequencing result of the target object according to the priority weight. In some embodiments of the description, an increasing frequency parameter and an attenuating frequency parameter can be set, each parameter can be configured with an individual step length, so that a priority weight score is calculated, a simple and efficient method for priority ranking recommendation can be quickly obtained, complicated processes such as big data acquisition and machine learning and costs associated with the processes are avoided, and meanwhile, the flexibility of adapting to different scenes is retained.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a flowchart illustrating an embodiment of a recommendation ranking processing method for a target object provided in the present specification;

fig. 2 is a block diagram of a server hardware structure to which a recommendation ranking process of a target object according to an embodiment of the present specification is applied;

fig. 3 is a schematic block configuration diagram of an embodiment of a recommendation sorting processing apparatus for target objects provided in this specification.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments in the present specification, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from one or more of the embodiments described herein without making any inventive step are intended to be within the scope of the present disclosure.

There are many scenarios in which recommendation ranking is required in software application development, product/service recommendation, and the like. For example, in a banking system, the payment/payment accounts most frequently used by customers may be recommended under different financial services (fund purchase, life payment, insurance application, etc.); recommending the most frequently used receiver in different account transfer and remittance scenes; recommending the most interesting financial products according to the preference of the client, and the like. The embodiment of the specification provides a recommendation method and a recommendation device which are simple and convenient and can be flexibly configured to adapt to different scene requirements. The device can be a user terminal, a server or an application development system. The device may include a single computer device, or may include a server cluster composed of a plurality of servers, or a server structure of a distributed system. The server may have a corresponding database or storage unit.

In the recommendation ranking processing method provided in one or more embodiments of the present specification, the influence of the increasing frequency (for example, the number of times of use) and the attenuation frequency (for example, the time lapse) on the ranking result is considered comprehensively, and the influence degree of the increasing frequency and the attenuation frequency is also considered. At least four types of parameters including increment frequency (increment frequency data), increment step size, decay frequency (decay frequency data), decay step size may be set in some embodiments to collectively act on the priority weight score. The increasing frequency may indicate a factor that may raise the ranking, such as a number of uses. The increment step size can represent the positive influence degree of the factor, for example, different increment frequencies correspond to increment step size values from 1 to 100, and values can be set according to service scenarios. The decay frequency may represent a factor that decreases the ranking, such as time (hours, minutes, seconds, days, weeks, months, years, etc. may be set). The attenuation step, like the increment step, may represent a negative influence of the factor, and may likewise vary from 1 to 100. The four parameters can calculate the final priority weight, the higher the weight score is, the higher the ranking is, and the more advanced the ranking is, the better the ranking is recommended.

Specifically, fig. 1 is a schematic flowchart of an embodiment of a recommendation sorting processing method for a target object provided in this specification. Although the present specification provides the method steps or apparatus structures as shown in the following examples or figures, more or less steps or modules may be included in the method or apparatus structures based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution order of the steps or the block structure of the apparatus is not limited to the execution order or the block structure shown in the embodiments or the drawings of the present specification. When the described method or module structure is applied to a device, a server or an end product in practice, the method or module structure according to the embodiment or the figures may be executed sequentially or in parallel (for example, in a parallel processor or multi-thread processing environment, or even in an implementation environment including distributed processing and server clustering).

In a specific embodiment, as shown in fig. 1, in an embodiment of a method for processing recommendation and ranking of a target object provided in this specification, the method may include:

s0: acquiring ascending frequency data and attenuation frequency data of a target object, wherein the ascending frequency data comprises a factor which is determined to be capable of increasing the sequencing priority, and the attenuation frequency data comprises a factor which is determined to be capable of reducing the sequencing priority;

s2: respectively determining an increment step length corresponding to the increment frequency data and an attenuation step length corresponding to the attenuation frequency data, wherein the increment step length represents influence degree data of factors on increasing the sequencing priority, and the attenuation step length represents influence degree data of factors on reducing the sequencing priority;

s4: determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length;

s6: and determining a recommendation sequencing result of the target object according to the priority weight.

In this embodiment, one or more pieces of increasing frequency data and one or more pieces of decreasing frequency data of the target object may be acquired. Meanwhile, the corresponding increment step length and attenuation step length of each increment frequency data and attenuation frequency data can be correspondingly set. The recommended priority weight can be calculated according to the parameters, and the recommended ranking result is determined according to the priority weight. Specifically, the fluctuation range of the priority weight of the target object can be calculated according to the increment frequency data, the increment step length, the attenuation frequency data and the attenuation step length; and adding the fluctuation amplitude and the priority weight of the target object last time to obtain the priority weight of the current sequence of the target object. The priority weighting may include a variety of implementations, such as a score, where a higher score indicates a higher priority for the recommendation. The number may be a number of a sequence, A, B, C, D indicating a priority, or the like, and may be specifically set according to a scene requirement.

Among the parameters described in some embodiments of the present specification:

the increment frequency data may specifically represent: quantitative data which has positive influence on priority improvement, such as use times, browsing times and the like, can be customized and expanded according to scenes;

the incremental step size may represent: increasing the influence degree of the frequency data on the priority, wherein the higher the value is, the deeper the influence is;

the decay frequency data may represent: quantitative data having a negative influence on priority improvement, such as the reduction of browsing times, the time length of a certain service in an application or an application which is not started, the time when a certain type of message is not viewed, and the like, can be customized and expanded according to a scene;

the attenuation step size may be expressed as: the influence degree of the attenuation frequency data on the priority is increased, and the influence is increased when the numerical value is increased;

in this embodiment, a specific implementation manner of determining the priority weight of the target object according to the increment frequency data, the increment step size, the attenuation frequency data, and the attenuation step size may include generating a weight formula by using a defined parameter, and calculating to obtain the priority weight of the target object. The specific weight formula may be implemented according to a scene in the embodiment. For example, in one example, the priority weight may be expressed as:

the priority weight is equal to the original priority weight plus the fluctuation range;

wherein, the fluctuation amplitude of the priority weight is calculated by adopting the following method:

the fluctuation width is (increment frequency data 1 × increment step 1+ increment frequency data 2 × increment step 2+ … + increment frequency data M × increment step M) - (attenuation frequency data 1 × attenuation step 1+ attenuation frequency data 2 × attenuation step 2+ … + attenuation frequency data M × attenuation step N), where M is the number of selected increment frequency data and N is the number of attenuation frequency data.

In the above embodiment, one or more parameter factors having a positive influence on the ranking and one or more parameter factors having a negative influence on the ranking are considered together, and the influence degrees of the factors are obtained correspondingly, and finally, the priority weights are obtained by adding the calculated influence degrees. The method considers the influence and the influence degree of a plurality of factors in many aspects, can output the recommendation sorting result of the priority sorting by relatively simple calculation, is simple and efficient, avoids complex processes such as big data acquisition and machine learning and the cost accompanying the complex processes, and simultaneously retains the flexibility of adapting to different scenes.

The target object described in this embodiment may correspond to the sort object or the target object associated with the sort object in the application scene in different application scenes. For example, in one embodiment, the target object may be a different business item in the application, such as an automobile insurance business, a financial business, a billing business, and the like. The different service items may periodically or aperiodically obtain message contents that need to be pushed to the user, and in a plurality of messages of the services, which services have higher priorities and need to be pushed or displayed, or which messages in a certain service have higher push priorities and how to order and recommend the messages is needed, which is one of the technical problems in an application scenario that can be solved by the embodiments of the present specification. In another application scenario, the target object may also be different applications in the terminal, for example, in the development design of a mobile phone system, the message display or prompt may be performed according to the use frequency of the applications in the system, for example, if a pan APP (application) is frequently used or browsed, and a kyoto APP is rarely used, the push priority of the message generated by the pan APP is higher than that of the kyoto APP. Specifically, in another embodiment of the method provided in this specification, the target object is a different service item in an application;

or,

the target objects are different applications in the terminal.

Of course, the foregoing embodiment may also include target object recommendation sorting processing in other application scenarios, and a person skilled in the art may reasonably extend to implementation schemes in other application scenarios based on the embodiment of the present specification, which is not described herein in detail.

In some embodiments of the present description, the data of increasing frequency may include the number of times of use, the number of times of browsing, and the like. In a specific embodiment, the increasing frequency data includes the number of times of actions of the target object by the user, where the number of times of actions may include the number of times of use and the number of times of browsing, and may also include other times such as the number of times of activation, the number of times of invocation, the number of times of touching, and the like. Likewise, in other embodiments, the decay frequency data may include a length of time that the target object is not acted upon by the user, such as 10 minutes, an hour, a day, 3 months, etc.

One preferred embodiment provided in the present specification calculates a change in the priority weight of the object in the current recommendation order using the number of times of use as an increasing frequency parameter and time as a decreasing frequency parameter. Specifically, in an embodiment of the method, the increasing frequency data includes a number of times of use of the target object, and the decreasing frequency data includes a number of days of time during which the target object is not used by the user;

number of uses increment step-days of time decay step.

The number of times of use and the number of days of time are parameters screened by the embodiment, which can obviously influence the result of the priority ranking. Therefore, the calculation result of the priority weight is more accurate, and the recommendation sequencing result is more reasonable.

In another implementation scenario, given that the application is typically used by multiple people, or as a group of multiple people in a business or organization, the identity of different users may have different requirements for the recommendation ranking. Therefore, in another embodiment of the method provided in this specification, the increment frequency data further includes: user job level of the user;

For example, the group leader has a higher priority than the group members to use an application, including opening an application, viewing a message, etc. Thus, when the message is facing a user that is a group long, there will be a higher recommendation priority than the group members. Therefore, the recommendation sequencing requirements under more different scenes can be met by adopting the scheme of the embodiment, the design is more flexible, and the method is adaptive to different scenes. One or more embodiments of the embodiment are adaptive to different scenes, and have better universality.

The method described above can also be used in another embodiment. If the scenario mostly occurs when the enterprise client uses the banking service, the enterprise client usually has operators with multiple roles, but the roles have different degrees of influence on the priority of the message, and the operators with authorized signature authority (such as corporate or manager) usually have higher weights, so that the roles can influence the priority of information recommendation in the scenario where multiple operators belong to the same client.

The present specification also provides another embodiment, and the usage time period can be increased as another increment frequency parameter. Specifically, in another embodiment of the method, the increasing frequency data further includes a single-use duration of the target object, and the decreasing frequency data includes a number of days of time during which the target object is not used by the user;

number of uses times increment step + single use duration times single increment step-days of time times days decay step.

The longer the time of a single use of a target object, the higher the demand or frequency of use of the target object, or the higher the dependency. The data is correspondingly used as the increasing frequency data, the weight of the sorting is increased, and the expected requirements of customers can be better met according to reasonable output recommendation results.

In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments.

In the recommendation ranking processing method for target objects provided in the embodiments of the present specification, the influence of the increasing frequency (for example, the number of times of use) and the fading frequency (for example, time lapse) on the ranking result may be considered comprehensively, and the influence degree of the increasing frequency and the fading frequency may also be considered. Determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length; and determining a recommendation sequencing result of the target object according to the priority weight. In some embodiments of the description, an increasing frequency parameter and an attenuating frequency parameter can be set, each parameter can be configured with an individual step length, so that a priority weight score is calculated, a simple and efficient method for priority ranking recommendation can be quickly obtained, complicated processes such as big data acquisition and machine learning and costs associated with the processes are avoided, and meanwhile, the flexibility of adapting to different scenes is retained.

By implementing the solution, the establishment of the recommendation mechanism can be quickly completed according to industry experience, the complexity is reduced, and excessive data support is not needed; the model formula is easy to understand, and the expansion and tuning can be conveniently carried out; meanwhile, the scheme of the embodiment is from the popular fourth paradigm recommending model dimension reduction to the third paradigm, a complex model learning process is not needed, and the investment cost is reduced.

The method embodiments provided in the embodiments of the present specification may be executed in a fixed terminal, a mobile terminal, a server, or a similar computing device. Taking an example of the application running on a server, fig. 2 is a block diagram of a hardware structure of a server to which a recommendation and ranking process of a target object according to an embodiment of the present disclosure is applied, where the hardware structure is greater or smaller. Specifically, as shown in fig. 2, the server 10 may include one or more (only one shown) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 2 is only an illustration and is not intended to limit the structure of the electronic device. For example, the server may also include more or fewer components than shown in FIG. 2, and may also include other Processing hardware, such as a GPU (Graphics Processing Unit), or have a different configuration than shown in FIG. 2, for example.

The memory 104 may be configured to store software programs and modules of application software, such as program instructions/modules corresponding to a recommendation sorting processing method for a target object in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, processes such as application, claim settlement, review, and payment of terminal screen insurance. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission module 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission module 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

Based on the recommendation and ranking processing method for the target object, the specification further provides a recommendation and ranking processing device for the target object. The apparatus may comprise a system (including a distributed system), software (applications), modules, components, servers, clients, etc. that utilize the methods described in the embodiments of the present specification in conjunction with any necessary equipment to implement the hardware. Based on the same innovative concept, the processing device in one embodiment provided in the present specification is as described in the following embodiment. Since the implementation scheme for solving the problem of the apparatus is similar to that of the method, the implementation of the specific processing apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Specifically, as shown in fig. 3, fig. 3 is a schematic block structure diagram of an embodiment of a device for processing recommendation and ranking of a target object provided in this specification, where the device may specifically include:

the sorting factor module 301 may be configured to obtain increasing frequency data and attenuation frequency data of the target object, where the increasing frequency data includes a factor determined to increase a sorting priority, and the attenuation frequency data includes a factor determined to decrease the sorting priority;

a step size determining module 302, configured to determine an increment step size corresponding to the increment frequency data and an attenuation step size corresponding to the attenuation frequency data, respectively, where the increment step size represents influence degree data of a factor on increasing the ranking priority, and the attenuation step size represents influence degree data of a factor on decreasing the ranking priority;

the weight calculation module 303 may be configured to determine the priority weight of the target object according to the increment frequency data, the increment step size, the attenuation frequency data, and the attenuation step size;

the recommendation calculation module 304 may be configured to determine a recommendation ranking result of the target object according to the priority weight.

Based on the description of the method, the present specification provides another embodiment of the apparatus, wherein the increasing frequency data includes the number of times the target object is acted on by the user.

Based on the foregoing description of the method, in another embodiment of the device provided in the present specification, the decay frequency data includes a length of time that the target object is not acted upon by the user.

Based on the description of the foregoing method, in another embodiment of the apparatus provided in this specification, the determining the priority weight of the target object according to the increment frequency data, the increment step size, the decay frequency data, and the decay step size includes:

Based on the foregoing description of the method, in another embodiment of the apparatus provided in the present specification, the fluctuation amplitude of the priority weight is calculated by:

Based on the description of the method, in another embodiment of the device provided in the present specification, the increasing frequency data includes the number of times of use of the target object, and the decreasing frequency data includes the number of days of time during which the target object is not used by the user;

number of uses increment step-days of time decay step.

In another embodiment of the apparatus provided in the present specification, based on the description of the foregoing method, the increment frequency data further includes: user job level of the user;

In another embodiment of the apparatus provided in the present specification, based on the description of the foregoing method, the increment frequency data further includes: the single-use duration of the target object, and the attenuation frequency data comprise the number of days of time that the target object is not used by a user;

Based on the related description of the foregoing method, in another embodiment of the apparatus provided in this specification, the target objects are different business items in an application;

or,

the target objects are different applications in the terminal.

It should be noted that the apparatus described above in the embodiments of the present disclosure may also include other embodiments according to the description of the related method embodiments. The specific implementation manner may refer to the description of the method embodiment, and is not described in detail herein.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Because the method is basically similar to the method embodiment, the description is simple, and the relevant points can be referred to partial description of the method embodiment.

The recommendation ranking processing apparatus for target objects according to the embodiments of the present specification may consider the influence of the increasing frequency (for example, the number of times of use) and the fading frequency (for example, the time lapse) on the ranking result, and also consider the degree of influence of the increasing frequency and the fading frequency. Determining the priority weight of the target object according to the increasing frequency data, the increasing step length, the attenuation frequency data and the attenuation step length; and determining a recommendation sequencing result of the target object according to the priority weight. In some embodiments of the description, an increasing frequency parameter and an attenuating frequency parameter can be set, each parameter can be configured with an individual step length, so that a priority weight score is calculated, a simple and efficient method for priority ranking recommendation can be quickly obtained, complicated processes such as big data acquisition and machine learning and costs associated with the processes are avoided, and meanwhile, the flexibility of adapting to different scenes is retained.

The method and apparatus for processing recommendation and ranking of target objects provided in the embodiments of this specification may be implemented by a processor executing corresponding program instructions in a computer, for example, implemented at a PC end using a c + + language of a windows operating system, or implemented in combination with necessary hardware using other application design languages corresponding to Linux, android, and iOS systems, or implemented in a distributed system-based multi-server process, or implemented in a quantum computer-based processing logic. Specifically, in an embodiment of the server implementing the method provided in this specification, the server may include a processor and a memory for storing processor-executable instructions, where the processor executes the instructions to implement:

The instructions described above may be stored on a variety of computer-readable storage media. The computer readable storage medium may include physical devices for storing information, which may be digitized and then stored using an electrical, magnetic, or optical media. The computer-readable storage medium according to this embodiment may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.

It should be noted that the apparatus described above in this embodiment of this specification may also include other embodiments according to the description of the related method or apparatus embodiment, and specific implementation manners may refer to the description of the method embodiment, which is not described in detail herein. For example, in one embodiment of an apparatus that may be implemented, the apparatus may be configured to include:

a. a parameter management module: uniformly managing the increasing frequency, the attenuation frequency, the increasing step length and the attenuation step length;

b. a data storage module: storing the frequency data as the source data of weight calculation;

c. a recommendation calculation module: and processing the source data by using a weight formula to generate an actual weight score, sorting the weight scores, and outputting a recommended sorting result.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Although the content of the embodiments of the present specification refers to operations and data descriptions of data acquisition, storage, interaction, calculation, judgment, and the like, such as weight calculation formula, number of usage times, time days, and the like, the embodiments of the present specification are not limited to the cases that are necessarily in compliance with the industry communication standard, the application design language, the standard data processing protocol, the communication protocol, and the standard network model/template, or the cases described in the embodiments of the present specification. Certain industry standards, or implementations modified slightly from those described using custom modes or examples, may also achieve the same, equivalent, or similar, or other, contemplated implementations of the above-described examples. The embodiments using these modified or transformed data acquisition, storage, judgment, processing, etc. may still fall within the scope of the alternative embodiments of the present description.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by user programming of the Device. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as ABEL (Advanced Boolean Expression Language), AHDL (alternate Hardware Description Language), traffic, CUPL (core universal Programming Language), HDCal, jhddl (Java Hardware Description Language), Lava, Lola, HDL, PALASM, rhyd (Hardware Description Language), and the like, which are currently used in the field-Hardware Language. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.

Claims

1. A recommendation ranking processing method for a target object, the method comprising:

2. The method of claim 1, wherein the incremental frequency data includes a number of times the target object is acted upon by the user.

3. The method of claim 1, wherein the decay frequency data includes a length of time that the target object is not acted upon by a user.

4. The method of claim 1, wherein determining the priority weight of the target object according to the increment frequency data, increment step size, decay frequency data, decay step size comprises:

5. The method of claim 4, wherein the fluctuation range of the priority weights is calculated by:

6. The method of claim 5, wherein the increasing frequency data includes a number of uses of the target object, and the decreasing frequency data includes a number of days of time that the target object is not used by the user;

number of uses increment step-days of time decay step.

7. The method of claim 5, the incremental frequency data further comprising: user job level of the user;

8. The method of claim 5, the incremental frequency data further comprising:

the single-use duration of the target object, and the attenuation frequency data comprise the number of days of time that the target object is not used by a user;

9. The method according to any of claims 1-8, wherein the target objects are different business items in an application;

or,

the target objects are different applications in the terminal.

10. A recommendation ranking processing apparatus for a target object, the apparatus comprising:

11. The apparatus of claim 10, wherein the incremental frequency data includes a number of times the target object is acted upon by the user.

12. The apparatus of claim 10, wherein the decay frequency data comprises a length of time that the target object is not acted upon by a user.

13. The apparatus of claim 10, wherein the determining the priority weight of the target object according to the increment frequency data, increment step size, decay frequency data, decay step size comprises:

14. The apparatus of claim 11, wherein the fluctuation range of the priority weights is calculated by:

15. The apparatus of claim 12, said increasing frequency data comprising a number of uses of the target object, said decreasing frequency data comprising a number of days of time that the target object is not used by the user;

number of uses increment step-days of time decay step.

16. The apparatus of claim 12, the increment frequency data further comprising: user job level of the user;

17. The apparatus of claim 12, the increment frequency data further comprising: the single-use duration of the target object, and the attenuation frequency data comprise the number of days of time that the target object is not used by a user;

18. The apparatus according to any one of claims 10 to 17, wherein the target objects are different business items in an application;

or,

the target objects are different applications in the terminal.

19. A recommendation server for a target object, comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implement: