Game recommendation method and device, electronic equipment and readable storage medium
By calculating game recommendation scores using user attributes and behavioral data, the problem of unpersonalization and accuracy of game recommendations in the prior art is solved, and more efficient game recommendations are achieved.
Patent Information
- Application Number
- CN202411533972.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-30
- Publication Date
- 2025-02-18
AI Technical Summary
In the recommendations of web games or instant mini games, the prior art cannot personalize the recommendations to users, and the recommendation accuracy is low.
By obtaining the target game sub-data and target game behavior data of the candidate game based on the attribute data and game behavior data of the current user, calculating the recommendation score, and then personalizing game recommendations.
It improves the accuracy of game recommendations, realizes personalized recommendations to users, makes full use of data resources, and simplifies the process of obtaining recommended scores.
Smart Images
Figure CN119455376A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of Internet technology, in particular to the field of artificial intelligence technology such as big data and cloud services. A game recommendation method, device, electronic device and readable storage medium are provided. Background Art
[0002] The existing technology usually recommends games based on a predetermined recommendation scheme without distinguishing specific users, especially when recommending web games or instant games. This has technical problems such as failure to make personalized recommendations for users and low recommendation accuracy. Summary of the invention
[0003] According to a first aspect of the present disclosure, a game recommendation method is provided, comprising: obtaining target game sub-data from game data of candidate games based on attribute data of a current user; obtaining target game behavior data from a game behavior data set of the current user based on the candidate games; obtaining a recommendation score of the candidate game based on the target game sub-data and the target game behavior data; and recommending a game to the current user based on the recommendation score of the candidate game.
[0004] According to a second aspect of the present disclosure, a game recommendation device is provided, comprising: a first acquisition unit, used to acquire target game sub-data from game data of candidate games according to attribute data of a current user; a second acquisition unit, used to acquire target game behavior data from a game behavior data set of the current user according to the candidate games; a processing unit, used to obtain a recommendation score of the candidate game according to the target game sub-data and the target game behavior data; and a recommendation unit, used to recommend a game to the current user according to the recommendation score of the candidate game.
[0005] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.
[0006] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method as described above.
[0007] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described above when executed by a processor.
[0008] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.
[0010] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;
[0011] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;
[0012] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;
[0013] Figure 4 It is a block diagram of an electronic device used to implement the game recommendation method of the embodiment of the present disclosure. DETAILED DESCRIPTION
[0014] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and mechanisms is omitted in the following description.
[0015] Figure 1 Schematic diagram of the first embodiment of the present disclosure. Figure 1 As shown, the game recommendation method of this embodiment specifically includes the following steps:
[0016] S101, acquiring target game sub-data from game data of candidate games according to attribute data of the current user;
[0017] S102, acquiring target game behavior data from the game behavior data set of the current user according to the candidate game;
[0018] S103, obtaining a recommended score of the candidate game according to the target game sub-data and the target game behavior data;
[0019] S104: Recommend games to the current user according to the recommendation scores of the candidate games.
[0020] The game recommendation method of this embodiment, on the one hand, obtains the target game sub-data and target game behavior data of the corresponding candidate games based on the game dimension, and then obtains the recommendation score for game recommendation based on the obtained relevant data, which can make full use of existing data resources, thereby simplifying the process of obtaining the recommendation score and improving the efficiency of obtaining the recommendation score. On the other hand, the recommendation score is obtained based on the relevant data that can reflect the user attributes and user behavior, which can improve the accuracy of the obtained recommendation score, and then improve the accuracy of the game recommendation, thereby achieving the purpose of making personalized game recommendations to users.
[0021] The game recommendation method provided in this embodiment can be applied to the recommendation of web games (i.e., web games), and can also be applied to the recommendation of instant mini-games in clients (such as clients of social applications, clients of information applications, etc.), that is, the candidate games in this embodiment can be web games or instant mini-games.
[0022] In this embodiment, the game data of the candidate games have multiple game data types, such as the click-through rate data of the game, the average game time data of the game, the user retention data of the game, the number of clicks data of the game, etc.
[0023] The game data corresponding to different game data types also include multiple game sub-data, and different game sub-data correspond to different attribute data; for example, in the game data type of "game click-through rate data", it includes game sub-data of "corresponding to female click-through rate data", game sub-data of "corresponding to male click-through rate data", game sub-data of "corresponding to city A", etc.
[0024] When executing S101 in this embodiment to obtain target game sub-data from the game data of candidate games based on the attribute data of the current user, the game sub-data corresponding to the attribute data of the current user can be obtained from the game data of the candidate games as the target game sub-data; the number of target game sub-data obtained when executing S101 in this embodiment can be one or more.
[0025] In this embodiment, the number of candidate games can be one or more; therefore, when executing S101, this embodiment can obtain at least one target game sub-data corresponding to one candidate game based on the attribute data of the current user, or can obtain at least one target game sub-data corresponding to multiple candidate games.
[0026] In this embodiment, the attribute data of the current user includes the gender, city and other data of the current user. Therefore, when executing S101, this embodiment can obtain game sub-data corresponding to the gender, game sub-data corresponding to the city, etc.; when obtaining the attribute data of the current user, this embodiment has obtained the user's consent or permission in advance, that is, the user knows that game recommendations will be made based on his or her own attribute data.
[0027] After executing S101 to obtain the target game sub-data, this embodiment executes S102 to obtain the target game behavior data from the game behavior data set of the current user according to the candidate games.
[0028] In this embodiment, the game behavior data set of the current user includes game behavior data corresponding to different candidate games, and the game behavior data set records the correspondence between different candidate games (such as identification information of different candidate games) and different game behavior data.
[0029] In this embodiment, the game behavior data corresponding to different candidate games have multiple behavior data types, such as the number of clicks of the current user on the candidate games, the game time data of the current user on the candidate games, the retention data of the current user on the candidate games, the number of views of the current user on the promotional information displayed in the candidate games, etc., that is, the game behavior data corresponds to the current user's historical game behavior on the candidate games within a preset time period (for example, within 3 days).
[0030] Likewise, when acquiring the game behavior data set of the current user, this embodiment has obtained the user's consent or permission in advance, that is, the user knows that game recommendations will be made based on the game behavior data.
[0031] When executing S102 to obtain target game behavior data from the game behavior data set of the current user according to the candidate games, this embodiment can first obtain the identification information of the candidate games, and then obtain the game behavior data corresponding to the identification information from the game behavior data set as the target game behavior data.
[0032] If the game behavior data set does not include game behavior data corresponding to the identification information of the candidate game, the target game behavior data acquired by executing S102 in this embodiment is empty.
[0033] It is understandable that this embodiment does not limit the execution order of step S101 and step S102, and the two steps can be performed separately or simultaneously.
[0034] After executing S102 to obtain the target game behavior data, this embodiment executes S103 to obtain the recommended score of the candidate game according to the target game sub-data and the target game behavior data.
[0035] If there are multiple candidate games, this embodiment will obtain the recommended score of each candidate game when executing S103.
[0036] When executing S103 of this embodiment to obtain the recommended score of the candidate game according to the target game sub-data and the target game behavior data, the implementation method that can be adopted is: obtaining the attribute score according to the target game sub-data; obtaining the behavior score according to the target game behavior data; and obtaining the recommended score of the candidate game according to the obtained attribute score and behavior score.
[0037] That is to say, this embodiment obtains the recommendation score of the candidate game based on the attribute score of the corresponding user attribute and the behavior score of the corresponding user behavior. Since more abundant target game sub-data and target game behavior data are used, the obtained recommendation score can reflect both the user attribute and the user behavior, thereby improving the accuracy of the obtained recommendation score, and then realizing personalized recommendation of the game based on the recommendation score.
[0038] When executing S103 to obtain the recommended score of the candidate game according to the attribute score and the behavior score, the implementation method that can be adopted in this embodiment is: obtaining a first weight value corresponding to the attribute score, and obtaining a second weight value corresponding to the behavior score. The first weight value and the second weight value in this embodiment are pre-set, and the first weight value is smaller than the second weight value to enhance the importance of the behavior score (i.e., user behavior) in obtaining the recommended score; obtaining the recommended score of the candidate game according to the attribute score and the first weight value, and the behavior score and the second weight value.
[0039] For example, if the first weight value is a and the second weight value is b, when executing S103, this embodiment can use the calculation result of (a×attribute score+b×behavior score) as the recommended score of the candidate game; in this embodiment, the first weight value a is 0.2 and the second weight value b is 0.8.
[0040] That is, this embodiment combines the weight values corresponding to different types of scores to obtain the recommended score, so that in the process of obtaining the recommended score, the different importance of the attribute score and the behavior score can be reflected, thereby further improving the accuracy of the obtained recommended score.
[0041] When executing S103 to obtain the attribute score according to the target game sub-data, the present embodiment may adopt the following implementation method: determine the attribute data type of the target game sub-data; obtain the attribute weight value corresponding to the target game sub-data according to the determined attribute data type; obtain the attribute score according to the target game sub-data and its corresponding attribute weight value.
[0042] For example, if the target game sub-data acquired by this embodiment is "the click-through rate data corresponding to females", then the attribute data type determined by executing S103 in this embodiment is "females". If the attribute weight value corresponding to "females" is X1, the attribute score obtained by executing S103 in this embodiment is "X1×the click-through rate data corresponding to females".
[0043] If the target game sub-data obtained by this embodiment is "the average gaming time data per capita corresponding to city A", then the attribute data type determined by executing S103 in this embodiment is "city A". If the attribute weight value corresponding to "city A" is X2, the attribute score obtained by executing S103 in this embodiment is "X2×the average gaming time data per capita corresponding to city A".
[0044] If the target game sub-data obtained in this embodiment are "corresponding female click-through rate data" and "corresponding average game duration data per capita in city A", then the attribute data types determined by executing S103 in this embodiment are "female" and "city A", and the attribute score obtained by executing S103 in this embodiment is "(X1×corresponding female click-through rate data)+(X2×corresponding average game duration data per capita in city A)".
[0045] If the target game sub-data acquired in this embodiment is "the click-through rate data corresponding to females" and "the click-through rate data corresponding to city A", then the attribute data types determined by executing S103 in this embodiment are "females" and "city A", and the attribute score obtained by executing S103 in this embodiment is "((X1×the click-through rate data corresponding to females)+(X2×the click-through rate data corresponding to city A)) / 2)".
[0046] That is to say, this embodiment also combines the attribute weight values corresponding to different attribute data types to obtain the attribute scores. In the process of obtaining the attribute scores, the importance of the game sub-data corresponding to different attributes can be reflected, thereby improving the accuracy of the obtained attribute scores.
[0047] When executing S103 to obtain a behavior score according to the target game behavior data, the present embodiment may adopt the following implementation method: determine the behavior data type of the target game behavior data; obtain a behavior weight value corresponding to the target game behavior data according to the determined behavior data type; and obtain a behavior score according to the target game behavior data and its corresponding behavior weight value.
[0048] For example, if the target game behavior data acquired by this embodiment is "the number of clicks of the current user", then the behavior data type determined by executing S103 in this embodiment is "the number of clicks of the current user on the candidate game", and if the behavior weight value corresponding to "the number of clicks of the current user on the candidate game" is Y1, the behavior score obtained by executing S103 in this embodiment is "Y1×the number of clicks of the current user".
[0049] If the target game behavior data acquired by this embodiment is "the game duration data of the current user", then the behavior data type determined by executing S103 in this embodiment is "the game duration data of the current user for the candidate game". If the behavior weight value corresponding to "the game duration data of the current user for the candidate game" is Y2, the behavior score obtained by executing S103 in this embodiment is "Y2×the game duration data of the current user".
[0050] If the target game behavior data acquired by this embodiment is "the number of clicks of the current user" and "the game time data of the current user", then the types of behavior data determined by executing S103 of this embodiment are "the number of clicks of the current user on the candidate games" and "the game time data of the current user on the candidate games", and the behavior score obtained by executing S103 of this embodiment is "(Y1×the number of clicks of the current user)+(Y2×the game time data of the current user)".
[0051] That is to say, this embodiment also combines the behavior weight values corresponding to different behavior data types to obtain behavior scores. In the process of obtaining behavior scores, the importance of game behavior data corresponding to different behaviors can be reflected, thereby improving the accuracy of the obtained behavior scores.
[0052] It can be understood that if the recommendation score of the candidate game obtained by executing S103 in this embodiment is 0, it indicates that the target game sub-data and target game behavior data corresponding to the current user cannot be obtained, that is, the current user is a new user, then this embodiment can obtain the recommendation score based on the game data of the candidate game.
[0053] Specifically, when executing S103, this embodiment can obtain the recommended score of the candidate game based on at least one of the candidate game's click-through rate data, average game time data per person, user retention data, and click count data, as well as game weight values corresponding to different game data types.
[0054] For example, if the game data in this embodiment is "game click-through rate data", the "game click-through rate data" is the click-through rate data of the candidate game corresponding to all user attributes (for example, when the user attribute is gender, the click-through rate data is the average of the "corresponding click-through rate data for females" and the "corresponding click-through rate data for males"). If the game weight value corresponding to the "click-through rate data" is Z1, the recommendation score obtained by executing S103 in this embodiment is (Z1×game click-through rate data).
[0055] After executing S103 to obtain the recommendation scores of the candidate games, this embodiment executes S104 to recommend games to the current user according to the recommendation scores of the candidate games.
[0056] When executing S104, this embodiment may sort at least one candidate game in descending order of recommendation score, and then recommend the top N candidate games as target games to the current user, where N is a positive integer greater than or equal to 1.
[0057] When executing S104, this embodiment may also compare the obtained recommendation score with a preset score threshold, and then recommend the candidate game as a target game to the current user when it is determined that the recommendation score is greater than or equal to the preset score threshold.
[0058] When executing S104 to recommend games to the current user, this embodiment can display the determined target game in the game recommendation area of the client or web page when it is detected that the user enters the game lobby or game center of the client or web page. For example, different target games can be displayed in different positions in the game recommendation area according to the sorting results.
[0059] For example, if the current user's city is City A, if the most popular candidate game in City A is Game B, and if the target game behavior data cannot be obtained (i.e., the current user has no historical game behavior), this embodiment will display Game B first in the game recommendation area.
[0060] If the current user's city is city A, if the most popular candidate game in city A is game B, and if the current user is a heavy enthusiast of game A, this embodiment will display game A in the first place in the game recommendation area and game B in the second place in the game recommendation area.
[0061] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure. Figure 2, a flowchart of the present embodiment when making game recommendations is shown in FIG. 1 : S201, collecting attribute data (such as gender, city, etc.) and game behavior data sets (each game behavior data in the game behavior data set corresponds to a candidate game) of the current user through user logs; S202, performing data cleaning on the collected attribute data and game behavior data sets; S203, obtaining game data of different candidate games, obtaining target game sub-data and target game behavior data of different candidate games corresponding to the current user; S204, obtaining recommendation scores of different candidate games according to target game sub-data and / or target game behavior data; or obtaining recommendation scores of different candidate games according to game data of different candidate games; S205, storing the obtained recommendation scores of different candidate games, for example, in MySQL on the server side; S206, when detecting that the current user enters the game lobby or game center in the client or web page, displaying different candidate games in the game recommendation area according to the stored recommendation scores.
[0062] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure. Figure 3 As shown, the game recommendation device 300 of this embodiment includes:
[0063] The first acquisition unit 301 is used to acquire target game sub-data from game data of candidate games according to attribute data of the current user;
[0064] A second acquisition unit 302 is used to acquire target game behavior data from the game behavior data set of the current user according to the candidate game;
[0065] The processing unit 303 is used to obtain the recommended score of the candidate game according to the target game sub-data and the target game behavior data;
[0066] The recommendation unit 304 is used to recommend games to the current user according to the recommendation scores of the candidate games.
[0067] When the first acquisition unit 301 acquires target game sub-data from the game data of the candidate games according to the attribute data of the current user, it can acquire the game sub-data corresponding to the attribute data of the current user from the game data of the candidate games as the target game sub-data; the number of target game sub-data acquired by the first acquisition unit 301 can be one or more.
[0068] In this embodiment, the number of candidate games can be one or more; therefore, the first acquisition unit 301 can acquire at least one target game sub-data corresponding to one candidate game based on the attribute data of the current user, or can acquire at least one target game sub-data corresponding to multiple candidate games respectively.
[0069] In this embodiment, the attribute data of the current user includes data such as the gender and city of the current user, so the first acquisition unit 301 can acquire game sub-data corresponding to the gender, game sub-data corresponding to the city, and so on.
[0070] In this embodiment, after the first acquisition unit 301 acquires the target game sub-data, the second acquisition unit 302 acquires the target game behavior data from the game behavior data set of the current user according to the candidate games.
[0071] In this embodiment, the game behavior data set of the current user includes game behavior data corresponding to different candidate games, and the game behavior data set records the correspondence between different candidate games (such as identification information of different candidate games) and different game behavior data.
[0072] In this embodiment, the game behavior data corresponding to different candidate games have multiple behavior data types, such as the number of clicks of the current user on the candidate games, the game time data of the current user on the candidate games, the retention data of the current user on the candidate games, the number of views of the current user on the promotional information displayed in the candidate games, etc., that is, the game behavior data corresponds to the current user's historical game behavior on the candidate games within a preset time period (for example, within 3 days).
[0073] Likewise, when acquiring the game behavior data set of the current user, this embodiment has obtained the user's consent or permission in advance, that is, the user knows that game recommendations will be made based on the game behavior data.
[0074] When the second acquisition unit 302 acquires target game behavior data from the game behavior data set of the current user according to the candidate game, it may first acquire identification information of the candidate game, and then acquire game behavior data corresponding to the identification information from the game behavior data set as the target game behavior data.
[0075] If the game behavior data set does not include the game behavior data corresponding to the identification information of the candidate game, the target game behavior data acquired by the second acquisition unit 302 is empty.
[0076] In this embodiment, after the second acquisition unit 302 acquires the target game behavior data, the processing unit 303 obtains the recommended score of the candidate game according to the target game sub-data and the target game behavior data.
[0077] If there are multiple candidate games, the processing unit 303 will obtain the recommendation score of each candidate game respectively.
[0078] When the processing unit 303 obtains the recommended score of the candidate game based on the target game sub-data and the target game behavior data, the implementation method that can be adopted is: obtain the attribute score based on the target game sub-data; obtain the behavior score based on the target game behavior data; obtain the recommended score of the candidate game based on the obtained attribute score and behavior score.
[0079] That is to say, the processing unit 303 obtains the recommendation score of the candidate game based on the attribute score of the corresponding user attribute and the behavior score of the corresponding user behavior. Since more abundant target game sub-data and target game behavior data are used, the obtained recommendation score can reflect both the user attribute and the user behavior, thereby achieving the purpose of personalized recommendation score acquisition and improving the accuracy of the obtained recommendation score.
[0080] When the processing unit 303 obtains the recommended score of the candidate game based on the attribute score and the behavior score, the implementation method that can be adopted is: obtaining a first weight value corresponding to the attribute score, and obtaining a second weight value corresponding to the behavior score; obtaining the recommended score of the candidate game based on the attribute score and the first weight value, and the behavior score and the second weight value.
[0081] That is, the processing unit 303 combines the weight values corresponding to different types of scores to obtain the recommended score, so that in the process of obtaining the recommended score, the different importance of the attribute score and the behavior score can be reflected, thereby further improving the accuracy of the obtained recommended score.
[0082] When the processing unit 303 obtains the attribute score based on the target game sub-data, the implementation method that can be adopted is: determine the attribute data type of the target game sub-data; obtain the attribute weight value corresponding to the target game sub-data according to the determined attribute data type; obtain the attribute score according to the target game sub-data and its corresponding attribute weight value.
[0083] That is to say, the processing unit 303 will also combine the attribute weight values corresponding to different attribute data types to obtain the attribute scores. In the process of obtaining the attribute scores, the importance of the game sub-data corresponding to different attributes can be reflected, thereby improving the accuracy of the obtained attribute scores.
[0084] When the processing unit 303 obtains the behavior score based on the target game behavior data, the implementation method that can be adopted is: determine the behavior data type of the target game behavior data; obtain the behavior weight value corresponding to the target game behavior data according to the determined behavior data type; obtain the behavior score according to the target game behavior data and its corresponding behavior weight value.
[0085] That is to say, the processing unit 303 will also combine the behavior weight values corresponding to different behavior data types to obtain the behavior scores. In the process of obtaining the behavior scores, the importance of the game behavior data corresponding to different behaviors can be reflected, thereby improving the accuracy of the obtained behavior scores.
[0086] It can be understood that if the recommended score of the candidate game obtained by the processing unit 303 is 0, it indicates that the target game sub-data and target game behavior data corresponding to the current user cannot be obtained, that is, the current user is a new user. In this embodiment, the recommended score can be obtained based on the game data of the candidate game.
[0087] Specifically, the processing unit 303 can obtain the recommended score of the candidate game based on at least one of the candidate game's click rate data, average game time data per person, user retention data, click count data, and game weight values corresponding to different game data types.
[0088] In this embodiment, after the processing unit 303 obtains the recommendation score of the candidate game, the recommendation unit 304 recommends the game to the current user according to the recommendation score of the candidate game.
[0089] The recommendation unit 304 may sort at least one candidate game in descending order of recommendation score, and then recommend the top N candidate games as target games to the current user, where N is a positive integer greater than or equal to 1.
[0090] The recommendation unit 304 may also compare the obtained recommendation score with a preset score threshold, and then recommend the candidate game as a target game to the current user if it is determined that the recommendation score is greater than or equal to the preset score threshold.
[0091] When recommending a game to the current user, the recommendation unit 304 may display the determined target game in the game recommendation area of the client or web page upon detecting that the user has entered the game lobby or game center of the client or web page, for example, displaying different target games in different positions in the game recommendation area according to the sorting results.
[0092] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0093] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
[0094] like Figure 4 , is a block diagram of an electronic device according to a game recommendation method according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0095] like Figure 4 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.
[0096] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0097] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as a game recommendation method. For example, in some embodiments, the game recommendation method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408.
[0098] In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the game recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the game recommendation method in any other appropriate manner (e.g., by means of firmware).
[0099] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0100] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable game recommendation device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.
[0101] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0103] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0104] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server for a distributed system, or a server combined with a blockchain.
[0105] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0106] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A game recommendation method, comprising: According to the attribute data of the current user, obtaining the target game sub-data from the game data of the candidate games; According to the candidate games, obtaining target game behavior data from the game behavior data set of the current user; Obtaining a recommended score for the candidate game according to the target game sub-data and the target game behavior data; Recommending games to the current user based on the recommendation scores of the candidate games.
2. The method according to claim 1, wherein: The step of obtaining the recommended score of the candidate game according to the target game sub-data and the target game behavior data includes: Obtaining attribute scores according to the target game sub-data; Obtaining a behavior score according to the target game behavior data; A recommendation score for the candidate game is obtained according to the attribute score and the behavior score.
3. The method according to claim 2, wherein: The step of obtaining the recommended score of the candidate game according to the attribute score and the behavior score includes: Obtaining a first weight value corresponding to the attribute score, and obtaining a second weight value corresponding to the behavior score; A recommended score for the candidate game is obtained according to the attribute score and the first weight value, and the behavior score and the second weight value.
4. The method according to claim 2, wherein: The obtaining of the attribute score according to the target game sub-data includes: Determine the attribute data type of the target game sub-data; According to the attribute data type, obtaining the attribute weight value corresponding to the target game sub-data; The attribute score is obtained according to the target game sub-data and its corresponding attribute weight value.
5. The method according to claim 2, wherein: The obtaining of the behavior score according to the target game behavior data includes: Determine the behavior data type of the target game behavior data; According to the behavior data type, obtaining a behavior weight value corresponding to the target game behavior data; The behavior score is obtained according to the target game behavior data and its corresponding behavior weight value.
6. The method according to claim 1, wherein: The step of acquiring target game sub-data from game data of candidate games according to attribute data of the current user includes: From the game data of the candidate game, game sub-data corresponding to the attribute data is acquired as the target game sub-data.
7. The method according to claim 1, wherein: The step of acquiring target game behavior data from the game behavior data set of the current user according to the candidate game comprises: Obtaining identification information of the candidate game; The game behavior data corresponding to the identification information is obtained from the game behavior data set as the target game behavior data.
8. A game recommendation device, comprising: A first acquisition unit, configured to acquire target game sub-data from game data of candidate games according to attribute data of a current user; A second acquisition unit, configured to acquire target game behavior data from the game behavior data set of the current user according to the candidate game; A processing unit, configured to obtain a recommended score of the candidate game according to the target game sub-data and the target game behavior data; A recommendation unit is used to recommend games to the current user according to the recommendation scores of the candidate games.
9. The device according to claim 8, wherein: When obtaining the recommended score of the candidate game according to the target game sub-data and the target game behavior data, the processing unit specifically performs: Obtaining attribute scores according to the target game sub-data; Obtaining a behavior score according to the target game behavior data; A recommendation score for the candidate game is obtained according to the attribute score and the behavior score.
10. The device according to claim 9, wherein: When the processing unit obtains the recommended score of the candidate game according to the attribute score and the behavior score, the processing unit specifically performs: Obtaining a first weight value corresponding to the attribute score, and obtaining a second weight value corresponding to the behavior score; A recommended score for the candidate game is obtained according to the attribute score and the first weight value, and the behavior score and the second weight value.
11. The device according to claim 9, wherein: When obtaining the attribute score according to the target game sub-data, the processing unit specifically performs: Determine the attribute data type of the target game sub-data; According to the attribute data type, obtaining the attribute weight value corresponding to the target game sub-data; The attribute score is obtained according to the target game sub-data and its corresponding attribute weight value.
12. The device according to claim 9, wherein: When obtaining the behavior score according to the target game behavior data, the processing unit specifically performs: Determine the behavior data type of the target game behavior data; According to the behavior data type, obtaining a behavior weight value corresponding to the target game behavior data; The behavior score is obtained according to the target game behavior data and its corresponding behavior weight value.
13. The device according to claim 8, wherein: When the first acquisition unit acquires the target game sub-data from the game data of the candidate games according to the attribute data of the current user, the first acquisition unit specifically performs: From the game data of the candidate game, game sub-data corresponding to the attribute data is acquired as the target game sub-data.
14. The device according to claim 8, wherein: When the second acquisition unit acquires the target game behavior data from the game behavior data set of the current user according to the candidate game, specifically: Obtaining identification information of the candidate game; The game behavior data corresponding to the identification information is obtained from the game behavior data set as the target game behavior data.
15. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.
16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.
17. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.