CN116173513A - Intelligent game pushing system and method - Google Patents

Intelligent game pushing system and method Download PDF

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Publication number
CN116173513A
CN116173513A CN202310442555.0A CN202310442555A CN116173513A CN 116173513 A CN116173513 A CN 116173513A CN 202310442555 A CN202310442555 A CN 202310442555A CN 116173513 A CN116173513 A CN 116173513A
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game
player
information
module
information data
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CN116173513B (en
Inventor
贾瑞
黄耀豪
曹迪
何瑞斌
石一峰
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Shenzhen Leyi Network Co ltd
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Shenzhen Leyi Network Co ltd
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    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/70—Game security or game management aspects
    • A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
    • A63F13/798—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for assessing skills or for ranking players, e.g. for generating a hall of fame
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/70—Game security or game management aspects
    • A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
    • A63F2300/55—Details of game data or player data management
    • A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
    • A63F2300/558—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history by assessing the players' skills or ranking

Abstract

The invention provides an intelligent game pushing system and method, comprising an information acquisition module, an information filtering module, an information classification module, an information processing module, an evaluation module, a feature extraction module, a prediction pushing module and a return visit feedback module; acquiring information of a game platform and a player, combining and filtering, and classifying by utilizing a multi-combination classification method; evaluating the player and the game platform by combining the processing result, then constructing a grid topological structure diagram, and comprehensively calculating the correlation of grid elements to push the game to the player; the invention realizes the technical effects of higher safety of the player information data and the game platform information data and more accurate game result pushing to the player.

Description

Intelligent game pushing system and method
Technical Field
The invention relates to the technical field of online games, in particular to an intelligent game pushing system and method.
Background
The reasonable game can help people develop intelligence, exercise thinking and reaction ability, training skills, cultivate rule consciousness and the like, and along with the increase of life pressure, the reasonable game becomes an important path for people to decompress. How to solve the problem that the game which is interesting and suitable for the players is particularly important in massive games, and win-win of game consumers and a game development platform can be achieved by adaptively and intelligently searching the games which the players like based on a big data mining technology and pushing the games to the players.
In the prior art, there is a game recommendation method based on big data, which mainly includes: constructing a game recommendation heterogram based on the player and the game history data, wherein nodes in the graph are players and games; calculating the correlation distance between the nodes; inputting the correlation distance between the player and the game history data and between the nodes into the GP neural network, and outputting the feature vectors of the player and the game history data through the embedded layer; the method comprises the steps of obtaining update weights when all feature vectors are convolved, and outputting action vectors of players and game history data through a GP neural network aggregation layer according to the update weights and the feature vectors; and outputting a recommendation result of the game through the GP neural network output layer by each behavior vector.
However, the above-mentioned technique has at least the following problems: the data information pushing results for players and games are poor in accuracy, and the player game platform information security is low.
Disclosure of Invention
The intelligent game pushing system and the intelligent game pushing method solve the problems of poor accuracy of pushing results and low information safety of a game platform of a player in the prior art, and achieve the technical effects that the information data of the player and the information data of the game platform are higher in safety, and the game results are pushed to the player more accurately.
The invention specifically comprises the following technical scheme:
an intelligent game push system, comprising:
the system comprises an information acquisition module, an information filtering module, an information classification module, an information processing module, an evaluation module, a characteristic extraction module, a prediction pushing module and a return visit feedback module;
the information acquisition module acquires player registration information and game platform registration input information, provides a data basis for the following game pushing, and transmits the acquired information data to the information filtering module for filtering; the information acquisition module is provided with an encryption component, and the information of the player and the game platform is encrypted by utilizing the existing encryption means;
the information filtering module is used for carrying out combined filtering processing on the information data of the player and the game platform obtained by the information obtaining module to obtain more effective information data, and transmitting the combined and filtered information data to the information classifying module;
the information classification module classifies the information according to the characteristics of the data information by adopting a multi-combination classification method to obtain classified data, facilitates the processing of the follow-up player information data and game data, sends the classified data to the player information processing module and the game information processing module for comprehensive processing, and evaluates the classified data by the player evaluation module and the game evaluation module;
the information processing module is used for comprehensively processing the classified information data to obtain a parameter influence factor set, and sending the processed parameter influence factor set to the evaluation module;
the evaluation module evaluates the player and the game platform according to the comprehensively processed parameter influence factors and the classified information data to obtain evaluation results of the player and the game platform, and sends the evaluation results to the prediction pushing module;
the feature extraction module is used for carrying out feature extraction on the classified information data to obtain the characteristic information of the player and the game platform, and sending the obtained feature information to the prediction pushing module;
the prediction pushing module predicts and pushes the player according to the parameter influence factor set of the information processing module, the evaluation result of the evaluation module, the characteristic information acquired by the characteristic extraction module and the feedback result acquired by the return visit feedback module;
and the return visit feedback module is used for obtaining feedback of the recommended result by carrying out questionnaire investigation on the player, feeding the feedback result back to the prediction pushing module and further adjusting the game pushing operation process of the player.
An intelligent game pushing method comprises the following steps:
s1, acquiring information of a game platform and a player through an information acquisition module, carrying out combined filtering processing on acquired information data, and carrying out classification processing on the filtered information data by utilizing a multi-combination classification method to obtain classified information data;
s2, carrying out information processing on the classified information data, simultaneously evaluating the player and the game platform by combining the processing result, carrying out feature extraction on the classified information data to obtain feature information of the player and the game platform, and constructing a grid topological structure diagram according to the processing result;
and S3, supplementing grid elements in the grid topological structure diagram by counting return visit feedback of each player to the game platform, comprehensively calculating the correlation of the grid elements, and pushing the game to the player to obtain a pushing result.
Further, the step S1 specifically includes:
the method comprises the steps of obtaining information data of a player and a game platform, carrying out encryption processing on the information data through an encryption component, carrying out combination filtering on the obtained information data, and carrying out information classification on the information data after the filtering processing by utilizing a multi-combination method to obtain the information data after the classification processing.
Further, the step S2 specifically includes:
respectively carrying out player information data processing and game platform information data processing on the classified information data to obtain a parameter influence factor set of a player related to a game; the information data set and the parameter influence factor set after the classification processing of the players and the game platform are used as training sequences of the deep learning neural network, the training sequences are trained by the existing deep learning neural network, and finally, assessment results of the players and the game are obtained, namely, an assessment set is obtained; and extracting the characteristics of the classified information data to obtain the characteristic information of the player and game platform data information, and obtaining a characteristic information set.
Further, the step S2 further includes:
and constructing a grid topology structure chart according to the parameter influence factor set, the characteristic information set and the evaluation set which are obtained after the player and game platform information data are processed.
Further, the step S2 further includes:
for classified player information data sets
Figure SMS_2
Game platform information data set after classification processing +.>
Figure SMS_3
Specifically, the->
Figure SMS_6
Wherein, the method comprises the steps of, wherein,Xrepresenting the total number of classification criteria for a set of player data, set +.>
Figure SMS_8
Any subset of (2) may be defined by +.>
Figure SMS_10
Indicating (I)>
Figure SMS_11
Represent the firstxUnder the individual classification criteria, player information data set, < ->
Figure SMS_12
；/>
Figure SMS_1
Wherein, the method comprises the steps of, wherein,Yrepresenting the total number of classification criteria for the game platform information data sets, set +.>
Figure SMS_4
Any subset of (2) may be defined by +.>
Figure SMS_5
Indicating (I)>
Figure SMS_7
Represent the firstyGame platform information data set, < +_, under individual classification criteria>
Figure SMS_9
The method comprises the steps of carrying out a first treatment on the surface of the And comprehensively analyzing and processing the classified information data set to obtain a parameter influence factor set related to the game by the player.
Further, the step S3 includes:
and calculating a feedback factor representing a feedback result by a fusion calculation method through feedback of a player on the game platform, transmitting feedback factor information to a prediction pushing module, supplementing the grid topological structure diagram, and further recommending games according to grid elements in the grid topological structure diagram.
Further, the step S3 further includes:
obtaining a feedback parameter set of a game platform from a questionnaire, wherein the feedback parameter set comprises a game playing period, a time input, a game attraction point, a game playing benefit, a game purpose and other relevant game questionnaire contents; each option of the questionnaire is assigned by an empirical method so as to facilitate the calculation of a feedback factor; in the first placenFor example, the game platform defines a feedback factor calculation formula as follows:
Figure SMS_13
wherein, the liquid crystal display device comprises a liquid crystal display device,Rrepresenting the total number of questions in the questionnaire,Cindicating the number of active persons participating in the questionnaire,
Figure SMS_14
represent the firstiProblem No.jPoints corresponding to individual player selections, +.>
Figure SMS_15
Representing the total score corresponding to the most preferred item of all questions in the questionnaire.
Further, the step S3 further includes:
according to the grid topological structure diagram constructed in the step S2, calculating the correlation of the associated elements according to the progressive relation of the grid elements, determining the correlation degree between the player and the game according to the correlation, and further determining the recommendation result, wherein the specific recommendation process is as follows:
the first step, calculating a first correlation degree of any player for any game;
calculating a relevant compensation value according to the parameter influence factor, the evaluation result, the feature vector and the feedback factor;
thirdly, determining a final correlation degree according to the first correlation degree and the correlation compensation value;
and fourthly, obtaining a correlation degree set of the player and each game platform, selecting an item with the largest correlation degree from the correlation degree sets to recommend the game for the player, and if a plurality of maximum values exist, recommending the game in time intervals.
The invention has at least the following technical effects or advantages:
1. according to the invention, the information of the player and the game platform is acquired, the information data is encrypted through the encryption component, so that the safety of the information data of the player and the game platform is ensured, the acquired information data is combined and filtered to obtain more accurate information data, so that the accuracy of a pushing result of the pushing system is ensured, and then the filtered information data is classified by utilizing a multi-combination method to obtain classified information data, so that a data basis is provided for subsequent data processing.
2. According to the invention, by constructing the grid topological structure diagram, factors influencing the pushing result are arranged in the grid, so that the expansion of a recommendation system is facilitated, the pushing result comprises an information data set after multi-combination classification processing, a parameter influence factor set related to a game by a player, characteristic information of information data of the player and a game platform and a feedback result obtained by return visit feedback, an accurate parameter basis is provided for game pushing, and a more accurate pushing result is further obtained in the game pushing system.
3. According to the invention, the correlation degree between the connected elements is calculated in the grid topological structure diagram, and updated and supplemented according to other influence elements to obtain more accurate correlation, the correlation between the player and the game is further determined, and the highest correlation is taken as a pushing result, namely, the more accurate pushing result, so that game pushing is completed.
Drawings
FIG. 1 is a block diagram of an intelligent game push system according to the present invention;
FIG. 2 is a flow chart of an intelligent game pushing method according to the invention;
fig. 3 is an exemplary diagram of a mesh topology according to the present invention.
Detailed Description
The embodiment of the invention solves the problems of poor accuracy of pushing results and lower information security of players and game platforms in the prior art by providing the intelligent game pushing system and the intelligent game pushing method, and the overall thinking is as follows:
firstly, acquiring information of a game platform and a player through an information acquisition module, carrying out combined filtering processing on acquired information data, and then carrying out classification processing on the filtered information data by utilizing a multi-combination classification method to obtain classified information data; then, carrying out information processing on the classified information data, simultaneously evaluating the player and the game platform by combining the processing result, carrying out feature extraction on the classified information data to obtain feature information of the player and the game platform, and constructing a grid topological structure diagram according to the processing result to provide basis for game pushing; and finally, supplementing grid elements in the grid topological structure diagram by counting return visit feedback of each player to the game platform, comprehensively calculating the correlation of the grid elements, and pushing the game to the player to obtain a more accurate pushing result.
The information data of the player and the game platform are acquired, the information data are encrypted through an encryption component, so that the safety of the information data of the player and the game platform is guaranteed, the acquired information data are combined and filtered to obtain more accurate information data, so that the accuracy of a recommendation result of the recommendation system is guaranteed, and then the information data after the filtering treatment are classified by a multi-combination method to obtain the information data after the classification treatment, so that a data base is provided for subsequent data treatment; the factors influencing the recommendation result are placed in the grid through constructing the grid topological structure diagram, so that the recommendation system is more convenient to develop, the influence recommendation result comprises information data sets which are subjected to multi-combination classification processing, parameter influence factor sets related to the game of a player, characteristic information of information data of the player and a game platform and feedback factors obtained through return visit feedback are calculated, accurate parameter basis is provided for game recommendation, and more accurate recommendation result is further obtained in the game recommendation system; the correlation between the connected elements is calculated in the grid topological structure diagram, and the correlation is updated and supplemented according to other influence elements to obtain more accurate correlation, so that the correlation between the player and the game is further determined, and the highest decorrelation is used as a recommendation result, namely a more accurate recommendation result, so that game recommendation is completed.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to fig. 1, the intelligent game pushing system of the invention comprises the following parts:
the system comprises an information acquisition module 010, an information filtering module 020, an information classification module 030, an information processing module 040, an evaluation module 050, a feature extraction module 060, a prediction pushing module 070 and a return visit feedback module 080;
the information acquisition module 010 acquires player registration information and game platform registration input information, provides a data basis for the following game pushing, and transmits acquired information data to the information filtering module 020 for filtering processing;
particularly, an encryption component is arranged in the information acquisition module 010 to encrypt information of the player and the game platform by utilizing the existing encryption means so as to ensure that the information of the player and the information of the game platform are not revealed and ensure the safety of a recommendation system;
the information filtering module 020 performs a combined filtering process on the information data of the player and the game platform obtained by the information obtaining module 010 to obtain more effective information data, and transmits the combined filtered information data to the information classifying module 030;
the information classification module 030 classifies information according to the characteristics of the data information by adopting a multi-combination classification method to obtain classified data, facilitates the processing of the follow-up player information data and game data, sends the classified data to the player information processing module and the game information processing module for comprehensive processing, and evaluates the classified data by the player evaluation module and the game evaluation module;
the information processing module 040 performs comprehensive processing on the classified information data to obtain a parameter influence factor set, and sends the processed parameter influence factor set to the evaluation module 050;
the evaluation module 050 evaluates the player and the game platform according to the comprehensively processed parameter influence factors and the classified information data to obtain evaluation results of the player and the game platform, and sends the evaluation results to the prediction pushing module 070;
the feature extraction module 060 performs feature extraction on the classified information data to obtain characteristic information of the player and the game platform, and sends the obtained characteristic information to the prediction pushing module 070;
the prediction pushing module 070 performs prediction pushing on the player according to the parameter influence factor set of the information processing module 040, the evaluation result of the evaluation module 050, the feature information acquired by the feature extraction module 060 and the feedback result acquired by the return visit feedback module 080;
the return visit feedback module 080 obtains feedback of the recommended result by performing questionnaire investigation on the player, and feeds the feedback result back to the prediction pushing module 070, so as to further adjust the game pushing operation process of the player to obtain a more accurate and humanized pushing result.
The intelligent game pushing method comprises the following steps:
s1, acquiring information of a game platform and a player through an information acquisition module 010, carrying out combined filtering processing on acquired information data, and then carrying out classification processing on the filtered information data by utilizing a multi-combination classification method to obtain classified information data;
s11, collecting information data of a player and a game platform to obtain an information data set of the player and the game platform, and carrying out combined filtering processing on the information data set to obtain filtered information data;
collecting the registration information of the player and the push information of the game platform to obtain an information data set
Figure SMS_18
，
Figure SMS_20
Wherein->
Figure SMS_24
Representing a set of player information data,/->
Figure SMS_19
，MRepresenting the number of players, set->
Figure SMS_23
Any subset of (a) may be made of->
Figure SMS_25
Indicating (I)>
Figure SMS_28
Represent the firstmInformation data set of individual players,/>
Figure SMS_16
The player information data comprises player age, player marital status, player family status, player game playing age, player gender, player income, ever-playing game category, playing game corresponding time length, telephone number, interesting game category and other relevant information data; />
Figure SMS_21
A game platform information data set is represented,
Figure SMS_26
，Nrepresenting the number of game platforms, set->
Figure SMS_29
Any subset of (2) may be defined by +.>
Figure SMS_17
Indicating (I)>
Figure SMS_22
Represent the firstnInformation data set of individual game platform, +.>
Figure SMS_27
The game platform information data comprises game types, game downloading amounts, game descriptions, game sharing times, game scores and other game information data; />
In particular, the collected information data is encrypted by the existing encryption technology, and only the user with the secret key calls the information data, and the secret key is handed to the recommended system administrator for storage, so that the safety of the information data of the player and the game platform is ensured, and a safer environment is provided.
For the collected information data set
Figure SMS_30
The combined filtering treatment is carried out, and the specific process is as follows:
firstly, setting different filtering criteria aiming at the characteristics of information data, wherein the filtering criteria comprise filtering, invalid filtering, conditional filtering, configuration filtering, verification filtering and the like; performing preliminary filtration on the collected information data set by utilizing a filtering criterion;
then, the information data set after preliminary filtering is subjected to final filtering processing by utilizing the prior noise reduction technology, so that a more accurate information data set is obtained
Figure SMS_31
；
As a specific embodiment, for the filter criteria, for player age, clipping filter criteria are employed: taking player information between 6 and 95 years old and deleting the player information of other ages;
finally, the information data set after combined filtering is obtained
Figure SMS_32
；
S12, classifying the combined and filtered information data, and laying a foundation for subsequent information data processing;
for the information data set after combination and filtration
Figure SMS_33
The method comprises the following steps of:
the first step, the information data set after the filtering processing is processed according to the data type
Figure SMS_34
Performing preliminary classification treatment, wherein the data types include characters, numerical values, images and audios;
setting different classification criteria to carry out final classification processing on the data after preliminary classification processing, wherein the classification criteria comprise player information classification criteria and game platform information classification criteria, and specifically include age group classification, interest and hobby classification, player gender classification, game category classification, game downloading amount classification and the like; obtaining information data set of multi-combination classification processing
Figure SMS_35
；
As a specific embodiment, when classifying the filtered player information data set in age groups, a segment is set every five years old, and the player information is classified in segments, so that a data base is provided for further processing.
Thirdly, obtaining the classified information data set
Figure SMS_36
；
According to the invention, the information of the player and the game platform is acquired, the information data is encrypted through the encryption component, so that the safety of the information data of the player and the game platform is ensured, the acquired information data is combined and filtered to obtain more accurate information data, so that the accuracy of a pushing result of the pushing system is ensured, and then the filtered information data is classified by utilizing a multi-combination method to obtain classified information data, so that a data basis is provided for subsequent data processing.
S2, carrying out information processing on the classified information data, simultaneously evaluating a player and a game platform by combining a processing result, carrying out feature extraction on the classified information data to obtain feature information of the player and the game platform, and constructing a grid topological structure diagram according to the processing result to provide basis for game pushing;
s21, respectively carrying out player information data processing and game platform information data processing on the classified information data to obtain a parameter influence factor set of a player related to a game;
for classified player information data sets
Figure SMS_37
Game platform information data set after classification processing +.>
Figure SMS_43
Specifically, the->
Figure SMS_48
Wherein, the method comprises the steps of, wherein,Xrepresenting a total number of classification criteria for the set of player information data, the set of classification criteria being classification criteria associated with the classification of player information among the classification criteria; set->
Figure SMS_39
Any subset of (2) may be defined by +.>
Figure SMS_42
Indicating (I)>
Figure SMS_44
Represent the firstxPlayer information data sets under individual classification criteria, +.>
Figure SMS_46
；/>
Figure SMS_38
Wherein, the method comprises the steps of, wherein,Yrepresenting the total number of classification criteria for the game platform information data sets, wherein the classification criteria for the game platform information data sets are the classification criteria related to game platform information classification in the classification criteria; set->
Figure SMS_41
Any subset of (2) may be defined by +.>
Figure SMS_45
Indicating (I)>
Figure SMS_47
Represent the firstyGame platform information data set under individual classification criteria, < ->
Figure SMS_40
The method comprises the steps of carrying out a first treatment on the surface of the And (3) comprehensively calculating the player information data and the game platform information data under each classification criterion to obtain a parameter influence factor set related to the game of the player, wherein the parameter influence factor comprises player preference, game labels, each player playing time, game downloading frequency, game entering frequency, game sharing frequency and the like.
As a specific embodiment, when calculating the conventional parameter impact factor, an existing conventional calculation method is adopted to calculate, for example, the time length for playing each game, the frequency of downloading the game, the number of game entries, the number of game shares, and the like, which are obtained by only counting by using a statistical method, in this application, the calculation formula for redefining the preference of the player, and the type of the game tag is as follows:
Figure SMS_49
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_52
represent the firstmPlayer pair 1nPreference of the individual game platform,/->
Figure SMS_54
Represent the firstnScoring of the individual games; />
Figure SMS_58
Represent the firstmPlayer pair 1nPreference of the individual game platform, < >>
Figure SMS_51
，IRepresent the firstmTotal expenditure of the individual player for playing the game in a certain period,/->
Figure SMS_55
Represent the firstmThe first player is within a certain periodnPayout in the individual games;
Figure SMS_57
represent the firstmPlayer pair 1nAttention of the individual game platform->
Figure SMS_59
，TRepresent the firstmTotal duration of each player's daily game play, +.>
Figure SMS_50
Represent the firstmThe individual player is on daynDuration in the respective games, P represents the firstmFrequency of individual players entering the game every day, +.>
Figure SMS_53
Represent the firstmThe individual player enters the first daynFrequency of individual games, S represents the firstmThe number of games shared by the individual players in a certain period, < >>
Figure SMS_56
Represent the firstmSharing the first player in a certain periodnThe number of games;
Figure SMS_60
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_61
represent the firstnGame number onelThe weight value of the individual tag(s),Numrepresent the firstnTotal number of players of the game checkup tab, < >>
Figure SMS_62
Represent the firstnIn-game hooklThe number of players on the tag;grepresent the firstnTotal score of the individual game hook tab, < +.>
Figure SMS_63
Represent the firstnIn-game hooklScoring the individual tags;
finally, obtaining parameter influence factor sets of the player and the game platform
Figure SMS_64
，/>
Figure SMS_65
Wherein, the method comprises the steps of, wherein,Krepresenting the number of parameter influencing factors, set->
Figure SMS_66
Any one of the elements may be defined by +.>
Figure SMS_67
Indicating (I)>
Figure SMS_68
Represent the firstkIndividual parameter influencing factors,/->
Figure SMS_69
。/>
In particular, among the parameter impact factor sets, there are parameter impact factors related to only the player, parameter impact factors related to only the game platform, and parameter impact factors related to the player and the game platform.
S22, evaluating the player and the game platform according to the parameter influence factor set obtained after the player and the game platform information data are processed and the player and the game platform information data to obtain evaluation results of the player and the game platform;
the method comprises the steps that an information data set and a parameter influence factor set of players and game platforms after classification processing are used as training sequences of a deep learning neural network, the training sequences are trained by the existing deep learning neural network, and the training sequences comprise a BP neural network, a GP neural network and the like; finally, the evaluation results of the player and the game, namely an evaluation set, are obtained
Figure SMS_73
，/>
Figure SMS_81
Representing a set of player evaluation results and a set of game platform evaluation results, respectively, wherein->
Figure SMS_83
Wherein, the method comprises the steps of, wherein,Mrepresenting the total number of players, set->
Figure SMS_72
Any one of the elements may be defined by +.>
Figure SMS_74
Indicating (I)>
Figure SMS_76
Represent the firstmEvaluation result of individual player->
Figure SMS_79
Comprises the evaluation values of each evaluation item of the player, includingJThe number of elements to be added to the composition,Jrepresenting the total number of items evaluated->
Figure SMS_71
；/>
Figure SMS_75
Wherein N represents the total number of game platforms, set +.>
Figure SMS_78
Any one of the elements may be defined by +.>
Figure SMS_80
Indicating (I)>
Figure SMS_70
Representing the evaluation result of the nth game platform, < +.>
Figure SMS_77
Comprises the evaluation values of each evaluation item of the game platform, which comprisesPThe number of elements to be added to the composition,Pindicating that the total number of terms is to be evaluated,/>
Figure SMS_82
。
s23, extracting characteristics of the classified information data to obtain characteristic information of player and game platform data information;
when the characteristic extraction is carried out on the classified information data, the existing characteristic extraction technology is adopted to obtain a characteristic vector set of the player and game platform data information
Figure SMS_84
，/>
Figure SMS_85
Wherein, the method comprises the steps of, wherein,Hrepresenting the total number of feature vectors, set->
Figure SMS_86
Any one of the elements may be defined by +.>
Figure SMS_87
Indicating (I)>
Figure SMS_88
Represent the firsthIndividual feature vectors->
Figure SMS_89
。
In particular, the feature vector set is comprised of a player feature vector set and a game platform feature vector set.
Further, according to the parameter influence factor set, the characteristic information set and the evaluation set obtained after the player and game platform information data are processed, a grid topology structure diagram is constructed, and the specific construction is as follows: the information data set after the multi-combination classification processing, the parameter influence factor set of the player and the game, the characteristic information set of the player and the game platform information data and the feedback result obtained by the return visit feedback are arranged in each grid point in the grid topological structure diagram, and referring to fig. 3, as a specific embodiment, the method adopts the following steps ofmIndividual player
Figure SMS_111
And (d)nPersonal game platform->
Figure SMS_97
For example, a->
Figure SMS_102
Representing player->
Figure SMS_96
Is the first of (2)xInformation data sets under the individual classification criteria; />
Figure SMS_98
Representing game platform->
Figure SMS_101
Is the first of (2)yInformation data sets under the individual classification criteria; />
Figure SMS_104
Is indicated at->
Figure SMS_94
In the case of classification only with the firstmA subset of individual player-related parameter impact factors; />
Figure SMS_99
Is indicated at->
Figure SMS_90
In the case of classification only with the firstnA subset of game platform-related parameter impact factors; />
Figure SMS_92
Representation->
Figure SMS_103
、
Figure SMS_105
A subset of parameter impact factors associated with the player and the game under the categorization; />
Figure SMS_107
Representation->
Figure SMS_110
Classification case NomEvaluation results of individual players; />
Figure SMS_100
Representation->
Figure SMS_109
Classification case NonThe evaluation results of the individual game platforms; />
Figure SMS_106
Representation->
Figure SMS_108
Classification case NomA feature vector set for each player; />
Figure SMS_91
Representation->
Figure SMS_93
Classification case NonFeature vector sets of the individual game platforms; />
Figure SMS_95
Representation of the first pairnFeedback factors for the individual game platforms;
in the grid topological graph, each grid point has other grid points related to the grid point, namely, all grid points are mutually connected, mutually influenced and mutually restricted, and a parameter basis is provided for game recommendation.
In particular, the feedback result consists of a feedback factor, which is introduced in step S3.
According to the invention, factors influencing the pushing result are placed in the grid by constructing the grid topological structure diagram, so that the expansion of a recommendation system is facilitated, the pushing result comprises an information data set after multi-combination classification processing, a parameter influence factor set related to a game by a player, characteristic information of information data of the player and a game platform and a feedback result obtained by return visit feedback, an accurate parameter basis is provided for game pushing, and a more accurate pushing result is further obtained in the game pushing system.
And S3, supplementing grid elements in the grid topological structure diagram by counting return visit feedback of each player to the game platform, comprehensively calculating the correlation of the grid elements, and pushing the game to the player to obtain a more accurate pushing result.
In particular, in the grid of the grid topology map, the associated grid elements are interconnected as part of the correlation calculation when making the prediction recommendation.
S31, through feedback of the return visit of the player to the game platform, a fusion calculation method is adopted to calculate and obtain a feedback factor representing a feedback result, and feedback factor information is transmitted to a prediction pushing module 070.
In the return visit feedback module 080, feedback factors are obtained by acquiring feedback of the player to the game platform from a questionnaire of the player and combining a fusion calculation method with an empirical method
Figure SMS_112
The specific process is as follows:
first, a feedback parameter set of a game platform is obtained from a questionnaire, wherein the feedback parameter set is used for a playerIncluding game play years, time invested, game attraction points, game play benefits, game goals, and other relevant game questionnaire content; in particular, each option of the questionnaire is assigned an empirical score to facilitate the calculation of the feedback factor; subsequently, in the first placenFor example, the game platform defines a feedback factor calculation formula as follows:
Figure SMS_113
wherein, the liquid crystal display device comprises a liquid crystal display device,Rrepresenting the total number of questions in the questionnaire,Cindicating the number of active persons participating in the questionnaire,
Figure SMS_114
represent the firstiProblem No.jPoints corresponding to individual player selections, +.>
Figure SMS_115
Representing total points corresponding to the most preferred items of all questions in the questionnaire;
s32, game recommendation is carried out according to grid elements in the grid topological structure diagram;
according to the grid topological structure diagram constructed in the step S2, calculating the correlation of the associated elements according to the progressive relation of the grid elements, determining the correlation degree between the player and the game according to the correlation, and further determining the recommendation result, wherein the specific recommendation process is as follows:
the first step, calculating a first correlation degree of any player for any game; the calculation formula is as follows
Figure SMS_116
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_117
representing a correlation between the mth player and the nth game platform between the xth player classification information data and the y game platform classification information data set by correlating the two classification information data setsAfter the statistics of the element items, the element items are calculated by an empirical method;
calculating a relevant compensation value according to the parameter influence factor, the evaluation result, the feature vector and the feedback factor; the calculation formula is as follows:
Figure SMS_118
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_119
expressed in the parameter influence factor set and the firstmIndividual player and the firstnSum of elements related to each game platform; />
Figure SMS_120
And the first in the player evaluation setmIndividual player and the firstnSum of elements related to the individual game platform +.>
Figure SMS_121
Representing the total of the evaluation results of the player, ->
Figure SMS_122
And in game platform assessment setnPersonal gaming platform and the thmSum of individual player related elements +.>
Figure SMS_123
Representing a game platform evaluation result sum;
Figure SMS_124
expressed in the feature vector set and the firstmIndividual player and the firstnInner products of feature vectors associated with the individual game platforms; />
Figure SMS_125
Representing a feedback factor;
thirdly, determining a final correlation degree according to the first correlation degree and the correlation compensation value; the calculation formula is as follows:
Figure SMS_126
finally, obtaining a correlation set of the player and each game platform, selecting the item with the largest correlation from the correlation sets to recommend the game for the player, and if a plurality of maximum values exist, recommending the game in time intervals.
According to the game push method, the correlation degree between the connected elements is calculated in the grid topological structure diagram, the correlation degree is updated and supplemented according to other influence elements, the more accurate correlation is obtained, the correlation between the player and the game is further determined, and the highest correlation is taken as a push result, namely the more accurate push result, so that game push is completed.
In summary, the intelligent game pushing system and the intelligent game pushing method are completed.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (9)

1. An intelligent game push system is characterized by comprising the following parts:
the system comprises an information acquisition module, an information filtering module, an information classification module, an information processing module, an evaluation module, a characteristic extraction module, a prediction pushing module and a return visit feedback module;
the information acquisition module acquires player registration information and game platform registration input information, provides a data basis for the following game pushing, and transmits the acquired information data to the information filtering module for filtering; the information acquisition module is provided with an encryption component, and the information of the player and the game platform is encrypted by utilizing the existing encryption means;
the information filtering module is used for carrying out combined filtering processing on the information data of the player and the game platform obtained by the information obtaining module and transmitting the combined and filtered information data to the information classifying module;
the information classification module classifies the information according to the characteristics of the data information by adopting a multi-combination classification method to obtain classified data, facilitates the processing of the follow-up player information data and game data, sends the classified data to the player information processing module and the game information processing module for comprehensive processing, and evaluates the classified data by the player evaluation module and the game evaluation module;
the information processing module is used for comprehensively processing the classified information data to obtain a parameter influence factor set, and sending the processed parameter influence factor set to the evaluation module;
the evaluation module evaluates the player and the game platform according to the comprehensively processed parameter influence factors and the classified information data to obtain evaluation results of the player and the game platform, and sends the evaluation results to the prediction pushing module;
the feature extraction module is used for carrying out feature extraction on the classified information data to obtain the characteristic information of the player and the game platform, and sending the obtained feature information to the prediction pushing module;
the prediction pushing module predicts and pushes the player according to the parameter influence factor set of the information processing module, the evaluation result of the evaluation module, the characteristic information acquired by the characteristic extraction module and the feedback result acquired by the return visit feedback module;
and the return visit feedback module is used for obtaining feedback of the recommended result by carrying out questionnaire investigation on the player, feeding the feedback result back to the prediction pushing module and further adjusting the game pushing operation process of the player.
2. The pushing method of an intelligent game pushing system according to claim 1, comprising the steps of:
s1, acquiring information of a game platform and a player through an information acquisition module, carrying out combined filtering processing on acquired information data, and carrying out classification processing on the filtered information data by utilizing a multi-combination classification method to obtain classified information data;
s2, carrying out information processing on the classified information data, simultaneously evaluating the player and the game platform by combining the processing result, carrying out feature extraction on the classified information data to obtain feature information of the player and the game platform, and constructing a grid topological structure diagram according to the processing result;
and S3, supplementing grid elements in the grid topological structure diagram by counting return visit feedback of each player to the game platform, comprehensively calculating the correlation of the grid elements, and pushing the game to the player to obtain a pushing result.
3. The pushing method of the intelligent game pushing system according to claim 2, wherein the step S1 specifically includes:
the method comprises the steps of obtaining information data of a player and a game platform, carrying out encryption processing on the information data through an encryption component, carrying out combination filtering on the obtained information data, and carrying out information classification on the information data after the filtering processing by utilizing a multi-combination method to obtain the information data after the classification processing.
4. The pushing method of the intelligent game pushing system according to claim 2, wherein the step S2 specifically includes:
respectively carrying out player information data processing and game platform information data processing on the classified information data to obtain a parameter influence factor set of a player related to a game; the information data set and the parameter influence factor set after the classification processing of the players and the game platform are used as training sequences of the deep learning neural network, the training sequences are trained by the existing deep learning neural network, and finally, assessment results of the players and the game are obtained, namely, an assessment set is obtained; and extracting the characteristics of the classified information data to obtain the characteristic information of the player and game platform data information, and obtaining a characteristic information set.
5. The pushing method of the intelligent game pushing system according to claim 4, wherein the step S2 further comprises:
and constructing a grid topology structure chart according to the parameter influence factor set, the characteristic information set and the evaluation set which are obtained after the player and game platform information data are processed.
6. The pushing method of the intelligent game pushing system according to claim 4, wherein the step S2 further comprises:
for classified player information data sets
Figure QLYQS_2
Game platform information data set after classification processing +.>
Figure QLYQS_7
Specifically, the->
Figure QLYQS_11
Wherein, the method comprises the steps of, wherein,Xrepresenting the total number of classification criteria for a set of player data, set +.>
Figure QLYQS_4
Any subset of (2) may be defined by +.>
Figure QLYQS_5
Indicating (I)>
Figure QLYQS_9
Represent the firstxUnder the individual classification criteria, player information data set, < ->
Figure QLYQS_12
；/>
Figure QLYQS_1
Wherein, the method comprises the steps of, wherein,Yrepresenting the total number of classification criteria for the game platform information data sets, set +.>
Figure QLYQS_6
Any subset of (2) may be defined by +.>
Figure QLYQS_8
Indicating (I)>
Figure QLYQS_10
Represent the firstyGame platform information data set, < +_, under individual classification criteria>
Figure QLYQS_3
The method comprises the steps of carrying out a first treatment on the surface of the General purpose medicineAnd comprehensively analyzing the classified information data set to obtain a parameter influence factor set related to the game by the player.
7. The pushing method of the intelligent game pushing system according to claim 2, wherein the step S3 specifically includes:
and calculating a feedback factor representing a feedback result by a fusion calculation method through feedback of a player on the game platform, transmitting feedback factor information to a prediction pushing module, supplementing the grid topological structure diagram, and further recommending games according to grid elements in the grid topological structure diagram.
8. The pushing method of an intelligent game pushing system according to claim 2, wherein the step S3 further comprises:
obtaining a feedback parameter set of a game platform from a questionnaire, wherein the feedback parameter set comprises a game playing period, a time input, a game attraction point, a game playing benefit, a game purpose and other relevant game questionnaire contents; each option of the questionnaire is assigned by an empirical method so as to facilitate the calculation of a feedback factor; in the first placenFor example, the game platform defines a feedback factor calculation formula as follows:
Figure QLYQS_13
wherein, the method comprises the steps of, wherein,Rrepresenting the total number of questions in the questionnaire,Cindicating the number of active persons participating in the questionnaire, < >>
Figure QLYQS_14
Represent the firstiProblem No.jPoints corresponding to individual player selections, +.>
Figure QLYQS_15
Representing the total score corresponding to the most preferred item of all questions in the questionnaire.
9. The pushing method of an intelligent game pushing system according to claim 2, wherein the step S3 further comprises:
according to the grid topological structure diagram constructed in the step S2, calculating the correlation of the associated elements according to the progressive relation of the grid elements, determining the correlation degree between the player and the game according to the correlation, and further determining the recommendation result, wherein the specific recommendation process is as follows:
the first step, calculating a first correlation degree of any player for any game;
calculating a relevant compensation value according to the parameter influence factor, the evaluation result, the feature vector and the feedback factor;
thirdly, determining a final correlation degree according to the first correlation degree and the correlation compensation value;
and fourthly, obtaining a correlation degree set of the player and each game platform, selecting an item with the largest correlation degree from the correlation degree sets to recommend the game for the player, and if a plurality of maximum values exist, recommending the game in time intervals.
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