WO2018133759A1

WO2018133759A1 - Ranking list generation method, computer device, and storage medium

Info

Publication number: WO2018133759A1
Application number: PCT/CN2018/072624
Authority: WO
Inventors: 曾庚卓; 郭慧; 吕远方; 张元超; 邱彬; 钟杨; 张冠君; 罗焱; 欧阳翰; 林孟光
Original assignee: 腾讯科技（深圳）有限公司
Priority date: 2017-01-23
Filing date: 2018-01-15
Publication date: 2018-07-26
Also published as: CN106874416A

Abstract

A ranking list generation method comprises: obtaining data amounts of data types associated with objects to be sorted; respectively performing normalization processing on the data amounts of the data types according to preset normalization rules respectively corresponding to the data types, so as to obtain normalized data amounts of the data types; according to the normalized data amounts of the data types and to the weighting coefficients of the data types, obtaining comprehensive weighted values of the objects to be sorted; and sorting the objects to be sorted according to the comprehensive weighted values of the objects to be sorted, obtaining a sorting result, and generating a ranking list according to the sorting result.

Description

Ranking list generation method, computer device and storage medium

This application is required to be submitted to the Chinese Patent Office on January 23, 2017, and the application number is 2017100515872. The application is entitled to the priority of the Chinese patent application entitled "The method for generating the leader list and the device for generating the list", the entire contents of which are incorporated by reference. In this application.

Technical field

The present application relates to the field of computer information processing technologies, and in particular, to a method for generating a ranking list and a device for generating a ranking list.

Background technique

With the development of information technology, it has become an important content in information technology applications to generate various related rankings for recommendation or to provide reference for end users' behavior. Taking the application installed in the terminal as an example, in order to meet the various needs of different end users, various applications emerge one after another. According to this, the application market provides downloads for various end users to provide various applications. Due to the different needs of different end users, the degree of attention to the application is also different, so that it can be reflected in different angles, for example, in terms of download volume and the like. Currently, the application market will sort the applications to obtain application ranking results and push them for end users to view, download, and so on.

At present, the more commonly used sorting methods are the sorting result obtained by manually configuring the running application and the sorting result by applying the sorting according to the download amount of the application. However, manually configuring the operational application to obtain the sorting result, the operator has a large workload, a strong subjective consciousness, and the evaluation criteria of different personnel are not uniform, resulting in inaccurate sorting results, resulting in inaccurate rankings. The method of sorting applications according to the download amount of the application can only reflect the statistical characteristics of the application under a single sorting basis, and the obtained sorting result is inaccurate, resulting in insufficient accuracy of the generated ranking list.

Summary of the invention

According to various embodiments provided by the present application, a ranking list generating method, a computer device, and a storage medium are provided.

To achieve the above objective, the embodiment of the present application adopts the following technical solutions:

A method for generating a leaderboard, comprising the following steps:

Obtaining the amount of data of each data type associated with the object to be sorted;

And normalizing the data amount of each of the data types according to a preset normalization rule corresponding to each of the data types, to obtain a normalized data amount of each of the data types;

Obtaining an integrated weight of the object to be sorted according to a normalized data amount of each of the data types and a weighting coefficient of each of the data types;

And sorting each of the objects to be sorted according to the comprehensive weights of the objects to be sorted, obtaining a sorting result, and generating a ranking list according to the sorting result.

A computer device comprising a memory and a processor, the memory storing computer readable instructions, the computer readable instructions being executed by the processor such that the processor performs the following steps:

One or more non-volatile storage media storing computer readable instructions, when executed by one or more processors, cause one or more processors to perform the following steps:

Details of one or more embodiments of the present application are set forth in the accompanying drawings and description below. Other features, objects, and advantages of the invention will be apparent from the description and appended claims.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present application. Other drawings may also be obtained from those of ordinary skill in the art in light of the inventive work.

1 is a schematic diagram of an application environment of an embodiment of the present application;

2 is a schematic diagram showing the internal structure of a computer device in an embodiment;

FIG. 3 is a schematic flowchart diagram of a method for generating a leader list according to an embodiment; FIG.

4 is a schematic flow chart of a method for generating a leader list according to another embodiment;

FIG. 5 is a schematic flowchart of obtaining data amount of each data type in a specific example; FIG.

6 is a schematic flow chart of a method for generating a ranking list according to another embodiment;

7 is a schematic diagram of a sorting result interface corresponding to a popular list sorting in a specific application example;

FIG. 8 is a schematic diagram of a sorting result interface corresponding to a new product list in a specific application example; FIG.

FIG. 9 is a schematic diagram of a sorting result interface corresponding to a hot sales list in a specific application example; FIG.

Figure 10 is a block diagram of a computer device of an embodiment;

11 is a block diagram of a data acquisition module in a computer device of a specific example;

Figure 12 is a block diagram of a computer device of another embodiment.

detailed description

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to limit the scope of the application.

FIG. 1 is a schematic diagram of an application environment in an embodiment of the present application. As shown in FIG. 1 , the application environment involves a terminal 10 and a server 20, and the terminal 10 and the server 20 can communicate through the network 30. The terminal 10 can access the corresponding server 20 through the network 30 to request a corresponding ranking list, and the ranking list has a sorting result of the corresponding object to be sorted, and the server 20 can push the ranking list to the terminal 10. The user of the terminal 10 refers to the ranking list and performs subsequent related operations. Taking the ranking list as an example of the application ranking list, the user of the terminal 10 can download, update, and the like according to the ranking list. The terminal 10 can be any device capable of implementing intelligent input and output, for example, a desktop computer or a mobile terminal, and the mobile terminal can be a smart phone, a tablet computer, a vehicle-mounted computer, a wearable smart device, or the like. The server 20 may be a server on which the platform providing the ranking list is located; the server 20 may be one or more. This embodiment relates to a scheme in which the server 20 sorts the objects to be sorted when generating the ranking list, and the server 20 may generate a corresponding ranking list based on the sorting result.

An internal structural diagram of a computer device in one embodiment is shown in FIG. The computer device can be the server 20. As shown in FIG. 2, the computer device includes a processor, memory, and network interface connected by a system bus. The memory includes a non-volatile storage medium and a memory, wherein the non-volatile storage medium of the computer device can store an operating system, a local database, and computer readable instructions that, when executed, cause the processor to execute A method for generating a leaderboard. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. Computer readable instructions may be stored in the memory of the computer device, the computer readable instructions being executable by the processor to cause the processor to perform a ranking list generation method. A network interface is used to connect and communicate with the network 30.

It will be understood by those skilled in the art that the structure shown in FIG. 2 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the server to which the solution of the present application is applied. The specific server may include a ratio. More or fewer components are shown in the figures, or some components are combined, or have different component arrangements.

Referring to FIG. 3, in an embodiment, a method for generating a ranking list is provided. The method can be run in the server shown in FIG. 1, and includes the following steps:

S310: Acquire an amount of data of each data type associated with the object to be sorted.

In this embodiment, the object may be a corresponding identifier of a video, a music, or the like, or may be a corresponding identifier of a game, an application, or the like. The object to be sorted refers to the object that needs to be sorted to obtain the sort result. The data volume of each data type associated with the object to be sorted is statistical data generated by performing related operations on the object to be sorted, for example, the total download amount of the object to be sorted within a specified time, and the number of times of the object to be sorted on the social platform. Then, different users download a certain object to be sorted, and the total download amount of the object to be sorted is counted, and the total download amount corresponding to the object to be sorted in a specified time can be obtained, due to the diversity of objects to be sorted. As well as the variety of data types, the amount of data of the data types corresponding to the different objects to be sorted can be obtained. It can be understood that the corresponding data types may also differ due to the different types of corresponding to be sorted.

S320: Perform normalization processing on the data amount of each data type according to a preset normalization rule corresponding to each data type, and obtain a normalized data amount of each data type.

Different data types can have different normalization rules. For example, the higher the total download volume, the higher the normalized value. The higher the number of shares, the higher the normalized value. . However, the total download amount and the number of shares are counted from different latitudes, and the two cannot be directly compared with each other. Therefore, the data amount of each data type can be uniformly normalized according to its corresponding preset normalization rule. A quantifiable value, that is, the normalized amount of data, that is, the data amount of different data types is unified into a quantifiable data, for example, can be normalized to a data amount interval [0, 1], that is, a single data type The data normalization interval is [0, 1].

For each data type, the amount of data can have its corresponding preset normalization rule. For example, the preset normalization rule corresponding to the total download amount may be a correspondence between the total download amount and the normalized data amount; the preset normalization rule corresponding to the share number may be the number of shares and the normalized data amount. Correspondence. In a specific application example, the correspondence between the data amount of each data type and the normalized data amount may be pre-stored, and the preset normalization rule is the data amount and the normalized data amount of each data type. Corresponding relationship, for each data type of data amount, the corresponding normalized data amount can be obtained according to the corresponding preset normalization rule. It can be understood that the preset normalization rule may also be a preset normalization calculation formula, that is, the normalized data amount corresponding to the data amount of each data type is calculated according to a preset normalization calculation formula. .

S330: Obtain the comprehensive weight of the object to be sorted according to the normalized data amount of each data type and the weighting coefficient of each data type.

Since the importance of each data type is different due to the different directions of interest, it is possible to set corresponding weighting coefficients for each data type, and perform weighted summation of weighting coefficients for the normalized data amount of each data type to obtain Comprehensive weight. Among them, the weighting coefficient indicates the importance degree of the corresponding data type, that is, the degree of influence on the comprehensive weight. The larger the value of the weighting coefficient, the more important the data type is, indicating that it plays an important role in the sorting process of the object to be sorted. . Highlight the characteristics of different data types in the sorting process by setting weighting coefficients.

In a specific application example, the following formula can be used to obtain the comprehensive weight of the object to be sorted:

Where s is the comprehensive weight, 1 ≤ i ≤ n, n is the number of types of the data type, L _i is the normalized data amount of the i-th data type, and f _i is the weighting coefficient of the i-th data type. among them,

S340: Sort the objects to be sorted according to the comprehensive weights of the objects to be sorted, obtain a sorting result, and generate a ranking list according to the sorting result.

After obtaining the comprehensive weight, since the comprehensive weight can represent the importance of the object to be sorted as a whole, according to the comprehensive weight of each object to be sorted, the objects to be sorted are sorted to obtain the sorting result, and the obtained sorting result can be intuitively Reflects the importance of each object to be sorted under the current sort.

The above-mentioned ranking list generation method firstly obtains the data quantity of each data type of the object to be sorted, and does not sort by a single data quantity, and can accurately reflect the overall characteristics of the object to be sorted, and separately for each data type. The amount of data is normalized to obtain the normalized data volume of each data type. Through normalization processing, the degree of attention of the objects to be sorted can be more intuitively reflected, and then the normalized data according to each data type can be obtained. The quantity and the weighting coefficient of each data type obtain the comprehensive weight of the object to be sorted, and the weighting coefficient reflects the importance of the data amount of each data type, so that the comprehensive weight of the object to be sorted can accurately reflect the object to be sorted. According to the comprehensive situation, the sorting result obtained by sorting the sorting objects according to the comprehensive weight value, based on the different weighting coefficients, can reflect the attention of the objects to be sorted according to the data volume of different data types, and then according to the comprehensive situation reflecting the comprehensive situation. The weights are sorted to obtain the sorting result, and the accuracy is high, according to the exact row The result of the order generates a ranking list to improve the accuracy of the ranking list.

In one of the examples, sorting of various sort types can be performed to obtain sort results for each sort type. FIG. 4 is a schematic flowchart diagram of a method for generating a ranking list in another embodiment. In this example, based on the embodiment shown in FIG. 3 above, a sorting result of multiple sorting types is obtained as an example for description. Wherein, when obtaining the sorting result of the multiple sorting types, for each different sorting type, there may be different weighting coefficients, that is, the weighting coefficients of the respective data types correspond to the sorting type. The weighting coefficient of the data type includes a weighting coefficient corresponding to each sorting type; the comprehensive weight of the object to be sorted includes a comprehensive weight corresponding to each sorting type; the sorting result includes a comprehensive synthesis of each sorting object of each sorting type. The ranking result obtained by sorting the weights; the ranking list includes the ranking list corresponding to each sorting type.

As shown in FIG. 4, in this embodiment, on the basis of the above-described embodiment shown in FIG. 3, after obtaining the normalized data amount of each data type in S320:

The step of obtaining the comprehensive weight of the object to be sorted according to the normalized data amount of each data type and the weighting coefficient of each data type in step S330 includes:

Step S431: obtaining, according to the normalized data amount of each data type, and the weighting coefficient of each data type corresponding to each sorting type, obtaining the comprehensive weight of the object to be sorted corresponding to each sorting type;

Step S340: Sorting each object to be sorted according to the comprehensive weight of each object to be sorted, obtaining a sorting result, and generating a ranking list according to the sorting result includes:

Step S441: Sorting each object to be sorted corresponding to each sorting type according to the weight of the integrated number of the object to be sorted corresponding to each sorting type, and obtaining the sorting result of each object to be sorted by each sorting type, according to each sorting type. The sorting result of each object to be sorted generates a ranking list corresponding to each sorting type.

Thus, when sorting, sorting can be performed for a plurality of sorting types to obtain different sorting results. For example, in this embodiment, the sorting type may include pop list sorting, new item list sorting, and hot list sorting, and depending on the sorting type, the attention degree of each data type is also different, and therefore, treating under different sorting types The sorting objects have different requirements when sorting, so each sorting type can correspond to different weighting coefficients, which is mainly reflected in that the same data type can have different weighting coefficients under different sorting types, that is, the specific weighting coefficients. The value corresponds to the sort type, so that the sort result corresponding to the sort type can be obtained. For example, in the sorting type of the popular list sorting, the data type of the total download amount corresponds to a weighting coefficient of 0.5, and the data object A in the final sorting result is ranked first. However, in the sorting type of the new product ranking, the data type of the total download amount corresponds to a weighting factor of 0.1. The data object B is ranked first in the final sorting result. This highlights the importance of different data types in different sort types, resulting in sort results corresponding to the sort type. The sorting result corresponding to the sorting type of the popular list corresponds to the more popular sorting objects on the current market. The sorting result corresponding to the sorting type of the new product list corresponds to the excellent sorting object of the current online market. The sorting result corresponding to the sorting type of the hot-selling list corresponds to the paying situation of the prominent user.

In one embodiment, the object to be sorted may include an application identifier. In this case, the data type may include any two or any combination of the following: the first total download amount in the most recent cycle period. a magnitude of change of the first total download amount relative to the second total download amount in the adjacent previous cycle time period, the score data, the first share number associated with the first social platform identifier, and the second social platform identifier The second sharing number, the sorting serial number on the predetermined third-party platform, the payment data in the first preset time period, the latest version update time, and the first preset time period is the same as or different from the most recent one-period time period. The first preset time period may be the same as the most recent one time period, or may be set to be different.

The most recent period of the current period may be the previous week of the current time, that is, the most recent one, and the adjacent previous period of time may be the previous period (previous week) adjacent to the most recent period of the previous period. For example, the object to be sorted includes the application identifier: the higher the first total download amount of the latest week of the corresponding application, the higher the normalized data amount of the corresponding total download amount of the corresponding week; the corresponding application The greater the increase in the first total download volume in the most recent week compared to the second total download volume in the previous week, the higher the normalized data volume of the change range; the higher the score data of the corresponding application in the local application market, the scoring data The higher the corresponding normalized data volume; the more times the corresponding application is shared to the first social platform in the most recent week (the first number of shares), the higher the normalized data volume corresponding to the first sharing number; The more times the corresponding application of the week is shared to the second social platform (the second number of shares), the higher the amount of normalized data corresponding to the second share number; the corresponding application is in the predetermined third-party application market. The higher the ranking, the higher the amount of normalized data corresponding to the sorting number in the scheduled third-party application market; the more the corresponding application in the most recent week, the paid data Normalization should be higher amount of data; the latest version of the corresponding application on-line time closer the current time, the higher the normalized latest version of the update time corresponding to the amount of data.

Referring to FIG. 5, in one embodiment, the step of obtaining the data amount of each data type associated with the object to be sorted includes:

S511: Acquire a data amount of the first data type from the local database.

It can be understood that the data amount of the first data type herein refers to the data that can be directly obtained from the server local database, wherein the local database herein may refer to a database located on the same device as the server, or may be Refers to data of a data type that is different from the current server but belongs to the same platform as the current server and can be directly accessed.

For example, the first data type may include: a first total download amount in a most recent cycle time period, and a second total download time in an adjacent previous cycle time period. The amount, the score data, the payment data in the first preset time period, any one of the first latest version update time, or any combination, the magnitude of the change according to the first total download amount in the most recent cycle time period, The second total download amount in the previous cycle period of the neighbor is determined.

S512: Obtain the data volume of the second data type from the third-party platform.

It can be understood that the data volume of the second data type herein refers to data that cannot be directly obtained from the server local database and needs to be obtained by using a third-party platform, wherein the third-party platform herein may be different from the current server. The related server device of the platform may also be a device that belongs to the same or associated platform as the current server, but cannot directly access the data amount of the data type from the device.

It can be understood that when the third-party platform acquires the data volume of the second data type, it can be obtained by using a web crawler crawling, for example, a sort serial number on a predetermined third-party platform.

For example, the second data type may include: a first sharing number associated with the first social platform identifier, a second sharing number associated with the second social platform identifier, and a predetermined number of times. Any one of the sort serial number, the second latest version update time, or any combination on the third-party platform.

The latest version update time is the first latest version update time or the second latest version update time. It can be understood that in the actual technical application, the first latest version update time may be directly used as the latest version update time based on actual needs, or the second latest version update time may be directly used as the latest version update time, or may be obtained. After the first latest version update time and the second latest version update time, the most recent version update time and the latest time of the second latest version update time are used as the latest version update time, which is not specifically limited in this embodiment.

Therefore, the data amount of each of the above data types can be obtained through different data sources, thereby ensuring the diversity of the data sources, thereby improving the accuracy of the obtained sorting results.

As described above, in the specific implementation of the solution of this embodiment, sorting of various sorting types may be performed, thereby obtaining sorting results of the respective sorting types. On the other hand, for the same sorting type, the corresponding weighting coefficients can also be adjusted as needed to adjust the importance of each data type to obtain more accurate sorting results. FIG. 6 is a schematic flow chart showing a method for generating a ranking list in another embodiment. In this example, based on the embodiment shown in FIG. 3, an adjustment of the weighting coefficient of the sorting type is required as an example.

As shown in FIG. 6, in the embodiment, on the basis of the embodiment shown in FIG. 3, in step S330, the object to be sorted is obtained according to the normalized data amount of each data type and the weighting coefficient of each data type. Before the step of comprehensive weighting, it also includes the steps:

S621: Receive a coefficient adjustment instruction, where the coefficient adjustment instruction includes a sort type identifier, and a weighting coefficient value to be updated of each data type corresponding to the sort type identifier;

S622: Update the weighting coefficients of each data type associated with the sorting type by using the weighting coefficient values to be updated of each data type corresponding to the sort type identifier.

It should be noted that, in the example shown in FIG. 6, the weighting coefficient is adjusted and updated after obtaining the normalized data amount in step S320. In the actual technical application, when the weighting coefficient corresponding to the sorting type is adjusted, it may be performed at any time, as long as the calculation of the comprehensive weight in step S330 is performed based on the updated weighting coefficient.

Therefore, in order to highlight the difference between different sorting types, the weighting coefficients corresponding to the data types in each sorting type may be different. In order to adapt to the changes of different requirements, the weighting coefficients of each data type corresponding to each sorting type may be performed. Adjust and update the weighting factors to meet changing needs.

The method for generating the above list list will be specifically described below with a specific embodiment. In the following specific example, the object to be sorted is taken as an example of the application identifier. When the object to be sorted is the application identifier, the ranking list obtained by ranking the application (the ranking list corresponding to the sorting type) may include a popular list, a new list, a hot list, and the like.

Taking the sorting type as the example of the popular list, in this case, the data amount of each of the above data types of the object to be sorted is first obtained, and then, according to the preset normalization rules corresponding to the respective data types, respectively, for each data type The amount of data is normalized to obtain the normalized data amount of each data type, and then according to the normalized data amount corresponding to the data amount of each data type and the weighting coefficient of each data type corresponding to the ranking of the popular list, Obtaining the comprehensive weights of the objects to be sorted, and finally sorting the sorted objects according to the comprehensive weights, obtaining the sorting result in the case of the ranking of the popular list, and generating a list corresponding to the sorting type according to the sorting result, in a specific example The generated popular list is shown in Figure 7. In the example shown in FIG. 7, the application IDs ranked in the top 4 are the A application identifier, the B application identifier, the C application identifier, and the D application identifier.

Similarly, the new product list including the sorting result of the object to be sorted corresponding to the ranking of the new product list is obtained by the above processing (as shown in FIG. 8), and the hot result of the sorting result including the object to be sorted corresponding to the hot-selling list ordering Sales list (as shown in Figure 9). The weighting coefficients corresponding to the data types under different sorting types are different, thereby reflecting the difference between the sorting results of different sorting types. For example, in the example shown in Fig. 8, the order of the top 4 in the ranking results of the new list is B application identification, E application identification, F application identification, and G application identification. In FIG. 9, the ranking results in the order of the hot list list are ranked in the top 4, followed by the D application identifier, the H application identifier, the I application identifier, and the J application identifier.

Therefore, by the method of the embodiment, starting from the data amount of different data types, normalizing the data to obtain a normalized data amount, and obtaining the comprehensive weight by the weighting coefficient and the normalized data amount, according to the comprehensive right The values are sorted to obtain sorting results, and different sorting types correspond to different weighting coefficients, that is, different sorting types can be generated by different weighting coefficients, thereby generating a list corresponding to the sorting type, for example, generating a popular list and a new list. And the hot list, highlighting the differences between the three lists, allowing users to more intuitively understand the attention of each object. Understandably, you can increase the data type and sort type according to your needs, and get a more comprehensive list.

In an embodiment, a computer device is further provided, and the internal structure of the computer device is as shown in FIG. 2, wherein the computer device is provided with a ranking list generating device, and the ranking list generating device includes each module, and each module includes The modules may be implemented in whole or in part by software, hardware or a combination thereof.

In an embodiment, a ranking list generating device is provided, as shown in FIG. 10, comprising: a data obtaining module 110, a normalization module 120, an integrated weighting module 130, and a sorting module 140.

The data obtaining module 110 is configured to acquire a data amount of each data type associated with the object to be sorted;

The normalization module 120 is configured to normalize the data amount of each data type according to a preset normalization rule corresponding to each data type, to obtain a normalized data amount of each data type;

The comprehensive weighting module 130 is configured to obtain the comprehensive weight of the object to be sorted according to the normalized data amount of each data type and the weighting coefficient of each data type;

The sorting module 140 is configured to sort the objects to be sorted according to the comprehensive weights of the objects to be sorted, obtain the sorting result, and generate a ranking list according to the sorting result.

The above-mentioned ranking list generating device firstly obtains the data amount of each data type of the object to be sorted, and does not sort by a single data amount, and can accurately reflect the overall characteristics of the object to be sorted, and separately for each data type. The amount of data is normalized to obtain the normalized data volume of each data type. Through normalization processing, the degree of attention of the objects to be sorted can be more intuitively reflected, and then the normalized data according to each data type can be obtained. The quantity and the weighting coefficient of each data type obtain the comprehensive weight of the object to be sorted. The weighting coefficient reflects the importance of the data quantity of each data type, so that the comprehensive weight of the object to be sorted can accurately reflect the object to be sorted. According to the comprehensive situation, the sorting result obtained by sorting the sorting objects according to the comprehensive weight value, based on the different weighting coefficients, can reflect the attention of the objects to be sorted according to the data volume of different data types, and then according to the comprehensive situation reflecting the comprehensive situation. The weights are sorted to obtain the sorting result, and the accuracy is high, according to the exact row The result of the order generates a ranking list to improve the accuracy of the ranking list.

In one embodiment, the weighting coefficients of the data types correspond to the sorting type, the weighting coefficients of the data type include weighting coefficients corresponding to the sorting types; the comprehensive weights of the objects to be sorted include and the sorting The sorting result includes a sorting result obtained by sorting the comprehensive weights of the objects to be sorted of each sorting type respectively; the ranking list includes a ranking list corresponding to each sorting type single.

In this embodiment, the comprehensive weighting module 130 may obtain the comprehensive weights of the objects to be sorted corresponding to the sorting types according to the normalized data amount of each data type and the weighting coefficients of each data type corresponding to each sorting type. .

At this time, the sorting module 140 may sort the objects to be sorted corresponding to the sorting types according to the comprehensive weights of the objects to be sorted corresponding to the sorting types, and obtain the sorting results of the objects to be sorted of each sorting type. A ranking list corresponding to each sorting type is generated according to the sorting result of each sorting object of each sorting type.

In one embodiment, the object to be sorted includes an application identifier, and the data type includes any two or any combination of the following: a first total download amount in a most recent cycle time period, the first a magnitude of change of a total download amount relative to a second total download amount in an adjacent previous cycle time period, score data, a first share number associated with the first social platform identifier, and a second social platform identifier associated with The number of times of sharing, the sorting number on the predetermined third-party platform, the payment data in the first preset time period, the latest version update time, the first preset time period is the same as the latest one-period time period or different.

Referring to FIG. 11 , in one embodiment, the data acquisition module 110 may include: a local data acquisition module 111 and a third-party data acquisition module 112 .

The local data obtaining module 111 is configured to obtain a data quantity of the first data type from the local database, where the first data type includes: a first total download quantity in a most recent cycle time period, and an adjacent previous cycle time period Any one or any combination of the second total download amount, the scoring data, the payment data in the first preset time period, the first latest version update time, and the change width according to the first total in the most recent cycle time period The download amount and the second total download amount in the adjacent previous cycle time period are determined;

The third-party data obtaining module 112 is configured to obtain a data quantity of the second data type from the third-party platform, where the second data type includes: a first sharing number associated with the first social platform identifier, and a second association with the second social platform identifier The number of sharing, any one of the sorting number on the predetermined third-party platform, and the second latest version update time, or any combination.

Referring to FIG. 12, in another embodiment, the foregoing ranking list generating apparatus may further include: a coefficient updating module 121.

The coefficient update module 121 is configured to receive a coefficient adjustment instruction, where the coefficient adjustment instruction includes a sort type identifier, a weighting coefficient value to be updated of each data type corresponding to the sort type identifier, and a weight to be updated of each data type corresponding to the sort type identifier The coefficient value updates the weighting coefficients for each data type associated with the sort type.

In an embodiment of the present application, a computer device is further provided, including a memory and a processor, where the computer stores readable instructions, where the computer readable instructions are executed by the processor, so that the processor performs the following steps: acquiring and waiting Sorting the data amount of each data type associated with the object; normalizing the data amount of each data type according to the preset normalization rule corresponding to each data type, and obtaining the normalized data amount of each data type According to the normalized data volume of each data type and the weighting coefficient of each data type, the comprehensive weights of the objects to be sorted are obtained; according to the comprehensive weights of the objects to be sorted, the objects to be sorted are sorted, and the sorting result is obtained. Generate a leaderboard based on the sorted results.

In one of the embodiments, the weighting coefficients of the data type include weighting coefficients corresponding to the respective sorting types;

The comprehensive weight of the object to be sorted includes the comprehensive weight corresponding to each sort type;

The sorting result includes a sorting result obtained by sorting the comprehensive weights of the objects to be sorted of each sorting type respectively;

The leaderboard list includes the leaderboards for each sorting type.

In one of the embodiments, the object to be sorted includes an application identifier;

The data type includes any two or any combination of the following: the first total download amount in the most recent cycle time period, and the first total download amount relative to the second total time in the adjacent previous cycle time period. The change amount of the download amount, the score data, the first share number associated with the first social platform identifier, the second share number associated with the second social platform identifier, and the sort number on the predetermined third-party platform, at the first preset The payment data in the time period, the latest version update time, the first preset time period is the same as or different from the most recent one time period.

In one of the embodiments, the step of obtaining the data amount of each data type associated with the object to be sorted includes:

Obtaining the data amount of the first data type from the local database, where the first data type includes: any one of the first total download amount, the second total download amount, the score data, the payment data, the first latest version update time, or any combination The change range is determined according to the first total download amount and the second total download amount;

Acquiring the amount of data of the second data type from the third-party platform, where the second data type includes: any one of the first sharing number, the second sharing number, the sorting serial number, and the second latest version update time, or any combination;

The latest version update time is the first latest version update time or the second latest version update time.

In one of the embodiments, the processor performs the steps of performing the step of: performing the weighting of the data according to each data type and the weighting coefficient of each data type to obtain the comprehensive weight of the object to be sorted:

Receiving a coefficient adjustment instruction, where the coefficient adjustment instruction includes a sort type identifier, and a weighting coefficient value to be updated of each data type corresponding to the sort type identifier;

The weighting coefficients of the data types associated with the sorting type are updated by the weighting coefficient values to be updated for each data type corresponding to the sort type identifier.

It should be understood that the various steps in the various embodiments of the present application are not necessarily performed in the order indicated by the steps. Except as explicitly stated herein, the execution of these steps is not strictly limited, and the steps may be performed in other orders. Moreover, at least some of the steps in the embodiments may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be executed at different times, and the execution of these sub-steps or stages The order is also not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of the other steps.

One of ordinary skill in the art can understand that all or part of the process of implementing the above embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a non-volatile computer readable storage medium. Wherein, the program, when executed, may include the flow of an embodiment of the methods as described above. Any reference to a memory, storage, database or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of formats, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronization chain. Synchlink DRAM (SLDRAM), Memory Bus (Rambus) Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM).

The technical features of the above embodiments may be arbitrarily combined. For the sake of brevity of description, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, It is considered to be the range described in this specification.

The above embodiments are merely illustrative of several embodiments of the present application, and the description thereof is more specific and detailed, but is not to be construed as limiting the scope of the claims. It should be noted that a number of variations and modifications may be made by those skilled in the art without departing from the spirit and scope of the present application. Therefore, the scope of the invention should be determined by the appended claims.

Claims

A method for generating a ranking list, which is characterized by being applied to a server, comprising the following steps:

Obtaining the amount of data of each data type associated with the object to be sorted;

And normalizing the data amount of each of the data types according to a preset normalization rule corresponding to each of the data types, to obtain a normalized data amount of each of the data types;

Obtaining an integrated weight of the object to be sorted according to a normalized data amount of each of the data types and a weighting coefficient of each of the data types;

And sorting each of the objects to be sorted according to the comprehensive weights of the objects to be sorted, obtaining a sorting result, and generating a ranking list according to the sorting result.
The method for generating a leaderboard according to claim 1, wherein

The weighting coefficient of the data type includes weighting coefficients corresponding to each sorting type;

The comprehensive weight of the object to be sorted includes a comprehensive weight corresponding to each of the sort types;

The sorting result includes a sorting result obtained by sorting the comprehensive weights of the objects to be sorted of each sorting type respectively;

The ranking list includes a ranking list corresponding to each of the sorting types.
The method for generating a leaderboard according to claim 2, wherein

The object to be sorted includes an application identifier;

The data type includes any two or any combination of the following: a first total download amount in a most recent cycle time period, and the first total download amount is relative to an adjacent previous cycle time period. The magnitude of the change in the second total download amount, the scoring data, the first share number associated with the first social platform identifier, the second share number associated with the second social platform identifier, and the sort number on the predetermined third party platform, The payment data in the first preset time period, the latest version update time, the first preset time period is the same as or different from the latest one time period.
The method for generating a ranking list according to claim 3, wherein the step of acquiring the data amount of each data type associated with the object to be sorted comprises:

Obtaining a data amount of the first data type from the local database, where the first data type includes: the first total download amount, the second total download amount, the rating data, the payment data, and the first latest version Any one or any combination of update times, the change magnitude being determined according to the first total download amount and the second total download amount;

Obtaining, by the third-party platform, the data volume of the second data type, where the second data type includes: any one of the first sharing number, the second sharing number, the sorting serial number, and the second latest version update time Item or any combination;

The latest version update time is the first latest version update time or the second latest version update time.
The method for generating a ranking list according to claim 2, wherein the comprehensive weight of the object to be sorted is obtained according to a normalized data amount of each of the data types and a weighting coefficient of each of the data types Before the value step, the steps are also included:

Receiving a coefficient adjustment instruction, where the coefficient adjustment instruction includes a sort type identifier, and a weighting coefficient value to be updated of each data type corresponding to the sort type identifier;

The weighting coefficients of the data types associated with the sorting type are updated with the weighting coefficient values to be updated for each data type corresponding to the sorting type identifier.
A computer apparatus comprising a memory and a processor, wherein the memory stores computer readable instructions, wherein the computer readable instructions are executed by the processor such that the processor performs the following steps:

Obtaining the amount of data of each data type associated with the object to be sorted;

And normalizing the data amount of each of the data types according to a preset normalization rule corresponding to each of the data types, to obtain a normalized data amount of each of the data types;

Obtaining an integrated weight of the object to be sorted according to a normalized data amount of each of the data types and a weighting coefficient of each of the data types;

And sorting each of the objects to be sorted according to the comprehensive weights of the objects to be sorted, obtaining a sorting result, and generating a ranking list according to the sorting result.
The computer apparatus according to claim 6, wherein the weighting coefficients of the data type comprise weighting coefficients corresponding to respective sorting types;

The comprehensive weight of the object to be sorted includes a comprehensive weight corresponding to each of the sort types;

The sorting result includes a sorting result obtained by sorting the comprehensive weights of the objects to be sorted of each sorting type respectively;

The ranking list includes a ranking list corresponding to each of the sorting types.
A computer apparatus according to claim 7, wherein

The object to be sorted includes an application identifier;

The data type includes any two or any combination of the following: a first total download amount in a most recent cycle time period, and the first total download amount is relative to an adjacent previous cycle time period. The magnitude of the change in the second total download amount, the scoring data, the first share number associated with the first social platform identifier, the second share number associated with the second social platform identifier, and the sort number on the predetermined third party platform, The payment data in the first preset time period, the latest version update time, the first preset time period is the same as or different from the latest one time period.
The computer device according to claim 8, wherein the step of acquiring the data amount of each data type associated with the object to be sorted comprises:

Obtaining a data amount of the first data type from the local database, where the first data type includes: the first total download amount, the second total download amount, the rating data, the payment data, and the first latest version Any one or any combination of update times, the change magnitude being determined according to the first total download amount and the second total download amount;

Obtaining, by the third-party platform, the data volume of the second data type, where the second data type includes: any one of the first sharing number, the second sharing number, the sorting serial number, and the second latest version update time Item or any combination;

The latest version update time is the first latest version update time or the second latest version update time.
A computer apparatus according to claim 7, wherein

And performing, by the processor, the step of: performing the step of: performing, according to the normalized data amount of each of the data types and the weighting coefficient of each of the data types, the integrated weight of the object to be sorted:

Receiving a coefficient adjustment instruction, where the coefficient adjustment instruction includes a sort type identifier, and a weighting coefficient value to be updated of each data type corresponding to the sort type identifier;

The weighting coefficients of the data types associated with the sorting type are updated with the weighting coefficient values to be updated for each data type corresponding to the sorting type identifier.
One or more non-volatile storage media storing computer readable instructions, wherein when the computer readable instructions are executed by one or more processors, cause one or more processors to perform the following steps:

Obtaining the amount of data of each data type associated with the object to be sorted;

And normalizing the data amount of each of the data types according to a preset normalization rule corresponding to each of the data types, to obtain a normalized data amount of each of the data types;

Obtaining an integrated weight of the object to be sorted according to a normalized data amount of each of the data types and a weighting coefficient of each of the data types;

And sorting each of the objects to be sorted according to the comprehensive weights of the objects to be sorted, obtaining a sorting result, and generating a ranking list according to the sorting result.
The storage medium according to claim 11, wherein the weighting coefficient of the data type comprises a weighting coefficient corresponding to each sorting type;

The comprehensive weight of the object to be sorted includes a comprehensive weight corresponding to each of the sort types;

The sorting result includes a sorting result obtained by sorting the comprehensive weights of the objects to be sorted of each sorting type respectively;

The ranking list includes a ranking list corresponding to each of the sorting types.
A storage medium according to claim 12, wherein

The object to be sorted includes an application identifier;

The data type includes any two or any combination of the following: a first total download amount in a most recent cycle time period, and the first total download amount is relative to an adjacent previous cycle time period. The magnitude of the change in the second total download amount, the scoring data, the first share number associated with the first social platform identifier, the second share number associated with the second social platform identifier, and the sort number on the predetermined third party platform, The payment data in the first preset time period, the latest version update time, the first preset time period is the same as or different from the latest one time period.
The storage medium according to claim 13, wherein the step of acquiring the data amount of each data type associated with the object to be sorted comprises:

Obtaining a data amount of the first data type from the local database, where the first data type includes: the first total download amount, the second total download amount, the rating data, the payment data, and the first latest version Any one or any combination of update times, the change magnitude being determined according to the first total download amount and the second total download amount;

Obtaining, by the third-party platform, the data volume of the second data type, where the second data type includes: any one of the first sharing number, the second sharing number, the sorting serial number, and the second latest version update time Item or any combination;

The latest version update time is the first latest version update time or the second latest version update time.
A storage medium according to claim 12, wherein

And performing, by the processor, the step of: performing the step of: performing, according to the normalized data amount of each of the data types and the weighting coefficient of each of the data types, the integrated weight of the object to be sorted:

Receiving a coefficient adjustment instruction, where the coefficient adjustment instruction includes a sort type identifier, and a weighting coefficient value to be updated of each data type corresponding to the sort type identifier;

The weighting coefficients of the data types associated with the sorting type are updated with the weighting coefficient values to be updated for each data type corresponding to the sorting type identifier.