CN111177562B

CN111177562B - Recommendation ordering processing method and device for target object and server

Info

Publication number: CN111177562B
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: 2024-04-19
Anticipated expiration: 2039-12-31
Also published as: CN111177562A

Abstract

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

Description

Recommendation ordering processing method and device for target object and server

Technical Field

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

Background

There are many scenarios in software application development that require recommendation ordering. For example, if an application generates a plurality of push messages of different services for a certain period of time, if the messages are all pushed to a user at the same time, poor application use experience is caused, so that the messages of the services often need to be ordered, and the message ranked at the top is selected as the message recommended to the user preferentially. Of course, there are other push requirements in different scenarios, such as new product experience, coupon release, etc., and optimization problems facing recommendation ordering are often required in application development or design.

The existing development recommendation mechanism is usually completed by adopting a machine learning mode and relying on big 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 data into a training set and a testing set;

b) Preprocessing the client behavior data, and performing supervised or unsupervised learning on clients to classify the clients in groups;

c) Selecting a multidimensional machine learning algorithm, and learning a recommendation model based on client grouping and behavior data;

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

The existing scheme requires a large amount of training data for machine learning, the model training process is complicated and long in time consumption, the machine learning computer resource consumption is large, and the acquisition cost of a large amount of data is high. And finally, the recommendation model is poor in readability and comprehensibility, 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 that can be more compact, reliable, and flexible to accommodate different scene requirements.

Disclosure of Invention

The embodiment of the specification aims to provide a recommendation ordering processing method, device and server for target objects, which can quickly complete establishment of a recommendation mechanism, obtain recommendation ordering results, reduce complexity of obtaining recommendation ordering and reduce cost and resource consumption.

The recommendation ordering processing method, device and server for the target object provided by the embodiment of the specification are realized in the following modes:

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

Acquiring increment frequency data and decay frequency data of a target object, wherein the increment frequency data comprises factors which are determined to increase the sorting priority, and the decay frequency data comprises factors which are determined to decrease the sorting priority;

Respectively determining an increment step length corresponding to the increment frequency data and a decay step length corresponding to the decay frequency data, wherein the increment step length represents the influence degree data of factors on the improvement of the sequencing priority, and the decay step length represents the influence degree data of factors on the reduction of the sequencing priority;

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

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

In one embodiment of the method, the incremental frequency data includes a number of actions of the target object by a user.

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

In one method embodiment, 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:

Calculating the fluctuation amplitude of the priority weight of the target object according to the increment frequency data, the increment step length, the attenuation frequency data and the attenuation step length;

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

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

Fluctuation amplitude= (increment frequency data 1 increment step 1+increment frequency data 2 increment step 2+ … increment frequency data M increment step M) - (decay frequency data 1 decay step 1+decay frequency data 2 decay step 2+ … +decay frequency data M decay step N), wherein M is the number of increment frequency data selected, and N is the number of decay frequency data;

Correspondingly, the priority weights of the current sequencing of the target objects are as follows:

Priority weight = original priority weight + fluctuation amplitude.

In one method embodiment, the incremental frequency data includes a number of uses of the target object, and the decaying frequency data includes a number of days of time the target object is not used by the user;

the fluctuation amplitude of the priority weight at least comprises the following steps of obtaining fluctuation data:

Number of times of use increment step-time days number of days decay step.

In one method embodiment, the increasing frequency data further includes: a user job level of the user;

Number of times of use increment step size + user job increment step size-time day x day decay step size.

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

Or alternatively

The target objects are different applications in the terminal.

A recommendation ordering processing device for a target object, the device comprising:

The sorting factor module is used for acquiring increasing frequency data and decreasing frequency data of the target object, wherein the increasing frequency data comprises factors which are determined to increase the sorting priority, and the decreasing frequency data comprises factors which are determined to decrease the sorting priority;

the step length determining module is used for determining an increment step length corresponding to the increment frequency data and an attenuation step length corresponding to the attenuation frequency data respectively, wherein the increment step length represents the influence degree data of factors on the improvement of the sequencing priority, and the attenuation step length represents the influence degree data of factors on the reduction of the sequencing priority;

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

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

In one embodiment of the apparatus, the incremental frequency data includes a number of actions of the target object by a user.

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

In one 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:

Priority weight = original priority weight + fluctuation amplitude.

In one embodiment of the apparatus, the incremental frequency data includes a number of uses of the target object, and the decaying frequency data includes a number of days of time the target object is not used by the user;

Number of times of use increment step-time days number of days decay step.

In an apparatus embodiment, the incremental frequency data further comprises: a user job level of the user;

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

Or alternatively

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 processor implementing when executing the instructions:

According to the recommendation ordering processing method, device and server for the target object, influence of increasing frequency (such as using times) and decreasing frequency (such as time lapse) on an ordering result can be comprehensively considered, and meanwhile the influence degree of increasing and decreasing is considered. Determining the priority weight of the target object according to the increment frequency data, the increment step length, the attenuation frequency data and the attenuation step length; and determining a recommendation ordering result of the target object according to the priority weight. In some embodiments of the specification, an increase frequency parameter and a decrease frequency parameter can be set, and each parameter can be configured with a single step length, so that a priority weight score is calculated, a simple and efficient priority ranking recommendation method can be quickly obtained, complicated processes such as big data acquisition and machine learning and the cost associated with the complex processes are avoided, and meanwhile, the flexibility of adapting to different scenes is reserved.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

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

FIG. 2 is a block diagram of a server hardware architecture to which a recommendation ordering process for target objects of embodiments of the present specification is applied;

Fig. 3 is a schematic block diagram of an embodiment of a target object recommendation ordering processing device provided in the present specification.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, 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 some embodiments, but not all embodiments in the present specification. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present application.

There are many scenarios in which recommendation ordering is required in software application development, product/business recommendation, and the like. For example, in banking systems, the payment/payment accounts most commonly used by customers may be recommended under different financial services (fund purchases, life payments, insurance applications, etc.); recommending the most commonly used payee in different transfer remittance scenes; and recommending the most interesting financial products according to the preference of the clients. The embodiment of the specification provides a simple recommendation method and device which can be flexibly configured to adapt to different scene requirements. The device can be a user terminal, a server or a system developed by application. The apparatus may comprise a single computer device, or may comprise a server cluster formed by a plurality of servers, or a server structure of a distributed system. The server may have a corresponding database or storage unit.

One or more embodiments of the present disclosure provide a method for processing a recommended ranking that comprehensively considers the influence of increasing frequency (such as the number of times of use) and decreasing frequency (such as the time lapse) on the ranking result, and also considers the influence degree of increasing and decreasing. In some embodiments, at least four parameters including increment frequency (increment frequency data), increment step size, decay frequency (decay frequency data), and decay step size may be set to work together with the priority weight score. The frequency of increment may represent a factor that would promote ordering, such as the number of uses. The increment step may represent the forward influence degree of the factor, for example, different increment frequencies correspond to increment step values from 1 to 100, and the value may be specifically set according to the service scenario. The decay frequency may represent a factor that may decrease the ranking, such as time (time, minutes, seconds, days, weeks, months, years, etc. may be set). The decay step size may represent the degree of negative influence of the factor, and may likewise vary from 1 to 100, with increasing step size. The four parameters can calculate the final priority weight, the higher the weight score, the higher the ranking, the more advanced the ranking, the more preferred it will be recommended.

Specifically, fig. 1 is a schematic flow chart of an embodiment of a method for processing recommendation ordering of a target object provided in the present specification. Although the description provides methods and apparatus structures as shown in the examples or figures described below, more or fewer steps or modular units may be included in the methods or apparatus, whether conventionally or without inventive effort. In the steps or the structures where there is no necessary causal relationship logically, the execution order of the steps or the module structure of the apparatus is not limited to the execution order or the module structure shown in the embodiments or the drawings of the present specification. The described methods or module structures may be implemented in a device, server or end product in practice, in a sequential or parallel fashion (e.g., parallel processor or multi-threaded processing environments, or even distributed processing, server cluster implementations) as shown in the embodiments or figures.

In an embodiment of a method for processing recommendation ordering of target objects provided in the present specification, as shown in fig. 1, the method may include:

S0: acquiring increment frequency data and decay frequency data of a target object, wherein the increment frequency data comprises factors which are determined to increase the sorting priority, and the decay frequency data comprises factors which are determined to decrease the sorting priority;

s2: respectively determining an increment step length corresponding to the increment frequency data and a decay step length corresponding to the decay frequency data, wherein the increment step length represents the influence degree data of factors on the improvement of the sequencing priority, and the decay step length represents the influence degree data of factors on the reduction of the sequencing priority;

S4: determining the priority weight of the target object according to the increment frequency data, the increment step length, the attenuation frequency data and the attenuation step length;

s6: and determining a recommendation ordering 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 increment step length and the attenuation step length corresponding to each increment frequency data and each attenuation frequency data can be correspondingly set. The recommendation priority weight can be calculated according to the parameters, and the recommendation is determined according to the priority weight to obtain the sequencing result. Specifically, the fluctuation amplitude of the priority weight of the target object can be calculated according to the increment frequency data, the increment step length, the decay frequency data and the decay step length; and adding the fluctuation amplitude with the priority weight of the last time of the target object to obtain the priority weight of the current sequencing of the target object. The priority weights may include a variety of implementations, such as a score, with higher scores indicating higher priority for the recommendation. The number may be a serial number, A, B, C, D representing priority, or the like, and may be specifically set according to the scene requirement.

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

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

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

The decay frequency data may represent: quantitative data with negative influence on the priority improvement, such as the amplitude reduction of browsing times, the time length of not starting an application or a certain service in the application, the moment of not checking a certain type of message, and the like, can be customized and expanded according to scenes;

The attenuation step size may represent: the influence degree of the attenuation frequency data on the priority is higher, and the influence is deeper;

in this embodiment, in an implementation manner of 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, the method may include generating a weight formula by using defined parameters, and calculating to obtain the priority weight of the target object. In an embodiment, the specific weight formula can be implemented according to the scene by adopting a corresponding implementation manner. For example, in one example, the priority weights may be expressed as:

priority weight = original priority weight + fluctuation amplitude;

The fluctuation amplitude of the priority weight is calculated by the following method:

Fluctuation amplitude= (increment frequency data 1 increment step 1+increment frequency data 2 increment step 2+ … increment frequency data M increment step M) - (decay frequency data 1 decay step 1+decay frequency data 2 decay step 2+ … +decay frequency data M decay step N), where M is the number of increment frequency data selected and N is the number of decay frequency data.

The above embodiment comprehensively considers one or more parameter factors with positive influence and one or more parameter factors with negative influence on the sorting, and the factors correspondingly obtain the influence degree, and finally calculates and adds the influence degree to obtain the priority weight. The method considers the influence and the influence degree of a plurality of factors in multiple aspects, can realize the output of the recommended sequencing result of the priority sequencing through relatively simple calculation, is simple and efficient, avoids the tedious processes of big data acquisition, machine learning and the like and the cost accompanying the complex processes, and simultaneously maintains the flexibility of adapting to different scenes.

The target object described in this embodiment may correspond to a sorting object or a target object associated with the sorting object in different application scenarios. For example, in one embodiment, the target object may be a different service item in the application program, such as a car insurance service, a financial service, a billing service, and the like. The different service items can obtain the content of the message which needs to be pushed to the user periodically or aperiodically, and the service which is higher in priority needs to be pushed or displayed in the multiple messages of the service, or what kind of message in a certain service needs to be pushed with higher priority, how to sort and recommend, which is one of the technical problems in an application scenario that can be solved by the embodiment 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, the panning APP (application) is frequently used or browsed, the barely used, and then the push priority of the message generated by the panning APP is higher than that of the barking APP. In particular, in another embodiment of the method provided in the present disclosure, the target object is a different service item in an application;

Or alternatively

The target objects are different applications in the terminal.

Of course, the above embodiment may further include target object recommendation ordering processing in other application scenarios, and those skilled in the art may reasonably extend to other application scenario offline implementation schemes based on the embodiment of the present disclosure, which is not described in detail herein.

In some embodiments of the present description, the incremental frequency data may include a number of uses, a number of browses, and the like. In a specific embodiment, the incremental frequency data includes the number of times the target object is acted on by the user, where the number of times may include the number of times of use and browsing described above, and may also include other times of activation, call, touch, and so on. Also, in other embodiments, the decay frequency data may include a length of time the target object is not acted upon by the user, such as 10 minutes, one hour, one day, 3 months, etc.

A preferred embodiment provided in this specification uses the number of times as an increment frequency parameter and uses time as a decay frequency parameter to calculate the change in the object priority weight when the current recommendation is ordered. Specifically, in one embodiment of the method, the incremental frequency data includes a number of uses of the target object, and the decaying frequency data includes a number of days of time the target object is not used by the user;

Number of times of use increment step-time days number of days decay step.

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

Another implementation scenario, considering that applications are typically used by multiple persons or commonly used as a group of multiple persons in an enterprise or organization, may have different needs for ranking recommendations for different users' identity roles. Thus, based on this, in another embodiment of the method provided herein, the increasing frequency data further comprises: a user job level of the user;

For example, a group leader may use an application with a higher priority than a group member, including opening an application, viewing a message, etc. Thus, when the user faced with the message is a group leader, there will be a higher recommendation priority than the group leader. Therefore, the proposal of the embodiment can meet the recommendation ordering requirements under more different scenes, has more flexible design and is suitable for different scenes. One or more embodiments of the present embodiment adapt to different scenarios, and have better versatility.

The method described above may also be applied in another implementation scenario. If the scenario is more in the case that an enterprise client uses a banking service, the enterprise client usually has operators with multiple job levels, and the different job levels have different degrees of influence on the priority of the information, and usually has operators with authorized signature authorities (such as enterprise legal persons or administrators), the weight is higher, so that in the scenario that multiple operators belong to the same client, the job levels can influence the priority of information recommendation.

The present specification also provides another embodiment, and may further increase the time of use as another incremental frequency parameter. Specifically, in another embodiment of the method, the incremental frequency data further includes a single use duration of the target object, and the decaying frequency data includes a number of days of time the target object is not used by the user;

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

The longer the single use time of the target object, the higher the demand or use frequency of the target object, or the higher the dependency. Correspondingly, the data are used as incremental frequency data, the sequencing weight is increased, and the expected demands of clients 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 identical and similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments. For relevance, see the description of the method embodiments.

According to the recommendation ordering processing method for the target object, influence of increasing frequency (such as using times) and decreasing frequency (such as time lapse) on ordering results can be comprehensively considered, and meanwhile the influence degree of increasing and decreasing is considered. Determining the priority weight of the target object according to the increment frequency data, the increment step length, the attenuation frequency data and the attenuation step length; and determining a recommendation ordering result of the target object according to the priority weight. In some embodiments of the specification, an increase frequency parameter and a decrease frequency parameter can be set, and each parameter can be configured with a single step length, so that a priority weight score is calculated, a simple and efficient priority ranking recommendation method can be quickly obtained, complicated processes such as big data acquisition and machine learning and the cost associated with the complex processes are avoided, and meanwhile, the flexibility of adapting to different scenes is reserved.

The implementation of the solution can quickly complete the establishment of a recommendation mechanism by referring to industry experience, reduce complexity and avoid excessive data support; the model formula is easy to understand, and expansion and optimization can be conveniently carried out; meanwhile, the embodiment reduces the dimension of the popular fourth-model recommended model to the third model, does not need a complex model learning process, and reduces the input cost.

The method embodiments provided in the embodiments of the present specification may be performed in a fixed terminal, a mobile terminal, a server, or similar computing device. Taking the example of running on a server, fig. 2 is a block diagram of a hardware structure of the server, with more or less hardware structures, to which a recommendation ordering process for target objects of the embodiments of the present specification is applied. In particular, as shown in fig. 2, the server 10 may include one or more (only one is shown in the figure) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 2 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the server may also include more or fewer components than shown in FIG. 2, for example, may also include other processing hardware, such as a GPU (Graphics Processing Unit, image processor), or have a different configuration than shown in FIG. 2.

The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to a target object recommendation ordering processing method in the embodiment of the present invention, and the processor 102 executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the processes of terminal screen insurance application, claim settlement, examination, claim payment, and the like. 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 remotely located relative to the processor 102, which may be connected to the computer terminal via 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. The 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 (Network Interface Controller, NIC) that can connect 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 for communicating with the internet wirelessly.

Based on the above-mentioned recommendation ordering processing method of the target object, the present disclosure further provides a recommendation ordering processing device of the target object. The apparatus may comprise a system (including a distributed system), software (applications), modules, components, servers, clients, etc. that employ the methods described in the embodiments of the present specification in combination with the necessary equipment means to implement the hardware. Based on the same innovative concept, the processing device in one embodiment provided in the present specification is described in the following embodiments. Because the implementation scheme and the method for solving the problem by the device are similar, the implementation of the specific processing device in the embodiment of the present disclosure may refer to the implementation of the foregoing method, and the repetition is omitted. While the means described in the following embodiments are preferably implemented in software, 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 diagram of an embodiment of a target object recommendation ordering processing device provided in the present disclosure, where the device may specifically include:

The ranking factor module 301 may be configured to obtain incremental frequency data and decremental frequency data of the target object, where the incremental frequency data includes a factor determined to raise a ranking priority, and the decremental frequency data includes a factor determined to lower the ranking priority;

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

The weight calculation module 303 may be configured to determine a 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;

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

In another embodiment of the apparatus provided herein, based on the description of the foregoing method, the incremental frequency data includes a number of actions of the target object by the user.

In another embodiment of the apparatus provided herein, based on the description of the foregoing method, the decay frequency data includes a length of time the target object is not acted upon by the user.

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

Based on the related description of the foregoing method, in another embodiment of the apparatus provided in the present specification, the fluctuation range of the priority weight is calculated in the following manner:

Priority weight = original priority weight + fluctuation amplitude.

Based on the related description of the foregoing method, in another embodiment of the apparatus provided in the present specification, the incremental frequency data includes a number of times the target object is used, and the decaying frequency data includes a number of days of time the target object is not used by the user;

Number of times of use increment step-time days number of days decay step.

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

In another embodiment of the apparatus provided in the present specification, based on the related description of the foregoing method, the increasing frequency data further includes: the single use duration of the target object, wherein the decay frequency data comprises the number of time days of the target object which are not used by a user;

In another embodiment of the apparatus provided in the present specification, the target object is a different service item in an application based on a related description of the foregoing method;

Or alternatively

The target objects are different applications in the terminal.

It should be noted that, the apparatus described in the embodiments of the present disclosure, the description of the embodiments of the related method may also include other implementations. Specific implementation may refer to description of method embodiments, and are not described herein in detail.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. Since it is substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in sections.

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

The method and the device for processing the recommended sorting of the target object provided in the embodiments of the present disclosure may be implemented in a computer by executing corresponding program instructions by a processor, for example, implemented on a PC side using the c++ language of a windows operating system, or implemented by combining other application design languages corresponding to Linux, android, iOS systems with necessary hardware, or implemented by multi-server processing based on a distributed system, or implemented by processing logic based on a quantum computer, or the like. Specifically, in an embodiment of a server for implementing the method provided in the present disclosure, the server may include a processor and a memory for storing instructions executable by the processor, where the implementation is performed by the processor when the instructions are executed:

The instructions described above may be stored in a variety of computer-readable storage media. The computer readable storage medium may include physical means for storing information, where the information may be stored electronically, magnetically, or optically, etc. The computer readable storage medium according to the present embodiment may include: means for storing information using electrical energy such as various memories, e.g., RAM, ROM, etc.; devices for storing information using magnetic energy such as hard disk, floppy disk, magnetic tape, magnetic core memory, bubble memory, and USB flash disk; devices for optically storing information, such as CDs or DVDs. Of course, there are other ways of readable storage medium, such as quantum memory, graphene memory, etc.

It should be noted that, the descriptions of the foregoing apparatuses according to the embodiments of the present disclosure may further include other implementations according to related methods or embodiments of the apparatuses, and specific implementation manners may refer to descriptions of the embodiments of the methods, which are not described herein in detail. For example, in one embodiment of the apparatus, it may be configured to include:

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

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

c. The recommendation calculation module: and processing the source data by using a weight formula, generating an actual weight score, sequencing the weight scores, and outputting a recommendation sequencing result.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a hardware+program class embodiment, the description is relatively simple, as it is substantially similar to the method embodiment, as relevant see the partial description of the method embodiment.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can 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 are also possible or may be advantageous.

Although operations and data descriptions such as weight calculation formulas, number of uses, number of days of time, etc. are mentioned in the embodiments of the present specification, operations and data descriptions such as interactions, calculations, decisions, etc. the embodiments of the present specification are not limited to the cases where compliance with industry communication standards, application design languages, standard data processing protocols, communication protocols, and standard network models/templates is necessary or the embodiments of the present specification are described. Some industry standards or embodiments modified slightly based on the implementation described by the custom manner or examples can also realize the same, equivalent or similar or predictable implementation effect after modification of the above examples. Examples of data acquisition, storage, judgment, processing, etc., using these modifications or variations are still within the scope of alternative embodiments of the present description.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (very-high-SPEED INTEGRATED Circuit Hardware Description Language) and verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of 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, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers 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 of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a car-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 the present description provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented in an actual device or end product, the instructions may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment) as illustrated by the embodiments or by 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, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element.

For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when implementing the embodiments of the present disclosure, the functions of each module may be implemented in the same or multiple pieces of software and/or hardware, or a module that implements the same function may be implemented by multiple sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

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

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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

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

The present embodiments 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 embodiments of the specification 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.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed 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, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

The foregoing is merely an example of an embodiment of the present disclosure and is not intended to limit the embodiment of the present disclosure. Various modifications and variations of the illustrative embodiments will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles 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 ordering processing method for a target object, the method comprising:

Determining a recommendation ordering result of the target object according to the priority weight;

the determining the priority weight of the target object according to the increment frequency data, the increment step length, the decay frequency data and the decay step length comprises the following steps:

Adding the fluctuation amplitude and the priority weight of the last time of the target object to obtain the priority weight of the current sequencing of the target object, wherein the target object is a sequencing object in the application scene or an object associated with the sequencing object in different application scenes, and the target object comprises: different service items in the application or the target object is different applications in the terminal;

wherein the increasing frequency data further comprises: a user job level of the user;

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

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

4. The method of claim 1, the current ordering of the target object having a priority weight of:

Priority weight = original priority weight + fluctuation amplitude.

5. The method of claim 1, the incremental frequency data comprising a number of uses of the target object, the decaying frequency data comprising a number of days of time the target object is not used by a user;

Number of times of use increment step-time days number of days decay step.

6. The method of claim 1, the increasing frequency data further comprising:

The single use duration of the target object, wherein the decay frequency data comprises the number of time days of the target object which are not used by a user;

7. A recommendation ordering processing device for a target object, the device comprising:

the recommendation calculation module is used for determining a recommendation ordering result of the target object according to the priority weight;

8. The apparatus of claim 7, wherein the incremental frequency data includes a number of actions of the target object by a user.

9. The apparatus of claim 7, wherein the decay frequency data comprises a length of time the target object is not acted upon by a user.

10. The apparatus of claim 7, the current ordering of the target object having a priority weight of:

Priority weight = original priority weight + fluctuation amplitude.

11. The apparatus of claim 7, the incremental frequency data comprising a number of uses of the target object, the decaying frequency data comprising a number of days of time the target object is not used by a user;

Number of times of use increment step-time days number of days decay step.

12. The apparatus of claim 7, the incremental frequency data further comprising: the single use duration of the target object, wherein the decay frequency data comprises the number of time days of the target object which are not used by a user;

13. A recommendation server for a target object, comprising a processor and a memory for storing processor executable instructions, the processor implementing when executing the instructions: