CN111084987B - Recommendation method and recommendation device for game props and computer readable storage medium - Google Patents

Recommendation method and recommendation device for game props and computer readable storage medium Download PDF

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Publication number
CN111084987B
CN111084987B CN201911140014.2A CN201911140014A CN111084987B CN 111084987 B CN111084987 B CN 111084987B CN 201911140014 A CN201911140014 A CN 201911140014A CN 111084987 B CN111084987 B CN 111084987B
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game
props
data
recommendation
recommended
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CN111084987A (en
Inventor
魏新宇
张攀
陈伦广
林培圻
陈伟健
罗勇刚
张嘉鑫
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Shenzhen Gameplay Technology Co ltd
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Shenzhen Gameplay Technology 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
    • A63F13/50—Controlling the output signals based on the game progress
    • A63F13/53—Controlling the output signals based on the game progress involving additional visual information provided to the game scene, e.g. by overlay to simulate a head-up display [HUD] or displaying a laser sight in a shooting game
    • A63F13/533—Controlling the output signals based on the game progress involving additional visual information provided to the game scene, e.g. by overlay to simulate a head-up display [HUD] or displaying a laser sight in a shooting game for prompting the player, e.g. by displaying a game menu
    • 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
    • A63F13/792—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for payment purposes, e.g. monthly subscriptions
    • 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
    • A63F13/80—Special adaptations for executing a specific game genre or game mode
    • 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
    • A63F2300/30—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 output arrangements for receiving control signals generated by the game device
    • A63F2300/308—Details of the user interface
    • 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
    • A63F2300/5513—Details of game data or player data management involving billing
    • 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
    • A63F2300/80—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 specially adapted for executing a specific type of game

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Business, Economics & Management (AREA)
  • Computer Security & Cryptography (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Optics & Photonics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention discloses a recommendation method of game props, which comprises the following steps: acquiring a current game scene; acquiring characteristic data of a user, wherein the characteristic data is obtained according to game data of the user, and the game data comprises at least one of identity information, game role information and historical behavior records of the user; determining props to be recommended according to the current game scene and the characteristic data; and outputting recommendation information according to the prop to be recommended. The invention also discloses a recommendation device and a computer readable storage medium for the game props, which are used for outputting prop information corresponding to the current game scene and the user characteristic data by determining the current game scene and the user characteristic data, and the recommendation of the game props can be changed according to different game scenes and users, so that the recommendation of the game props is more accurate.

Description

Recommendation method and recommendation device for game props and computer readable storage medium
Technical Field
The present invention relates to the technical field of game props, and in particular, to a recommendation method, a recommendation device and a computer readable storage medium for game props.
Background
In games such as a fighting land owner, a plurality of different kinds of game props exist for users to purchase and use, and different special functions are provided for the current game roles of the users. However, current play objects are typically recommended to the user as a fixed play object or objects, resulting in inaccurate recommendations of play objects.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The main purpose of the present invention is to provide a recommendation method, a recommendation device and a computer readable storage medium for game props, which aim to improve the accuracy of game props recommendation by recommending game props corresponding to the current game scene and user characteristic data to users.
In order to achieve the above object, the present invention provides a recommendation method for game props, the recommendation method for game props comprising the steps of:
acquiring a current game scene;
acquiring characteristic data of a user, wherein the characteristic data is obtained according to game data of the user, and the game data comprises at least one of identity information, game role information and historical behavior records of the user;
determining props to be recommended according to the current game scene and the characteristic data;
and outputting recommendation information according to the prop to be recommended.
Optionally, the step of acquiring the current game scene includes:
acquiring interface information of a current game interface;
and determining the current game scene according to the interface information.
Optionally, the step of acquiring the feature data of the user includes:
obtaining game data corresponding to the user;
performing dimension reduction processing on the game data to obtain feature vectors;
and obtaining the characteristic data according to the characteristic vector.
Optionally, the step of performing dimension reduction processing on the game data to obtain a feature vector includes:
descriptive statistics are performed on the game data to determine missing values and outliers in the game data;
optimizing the game data according to the missing values and the outliers;
and performing dimension reduction processing on the optimized game data to obtain feature vectors.
Optionally, the step of determining the prop to be recommended according to the current game scene and the feature data includes:
acquiring preset characteristic data corresponding to the current game scene;
calculating the similarity between the characteristic data and the preset characteristic data;
determining target feature data in the preset feature data according to the similarity;
and determining the props to be recommended according to the game props corresponding to the target characteristic data.
Optionally, the step of determining the prop to be recommended according to the game prop corresponding to the target feature data includes:
acquiring owned props corresponding to the users;
and determining the props to be recommended according to the game props corresponding to the target characteristic data and the owned props.
Optionally, after the step of outputting the recommendation information according to the prop to be recommended, the method further includes:
when a selection instruction triggered by the recommendation information is detected, determining a game prop corresponding to the selection instruction;
and updating the preset characteristic data according to the game props corresponding to the selection instruction and the characteristic data.
Optionally, the step of outputting recommendation information according to the prop to be recommended includes:
when a plurality of props to be recommended exist, sorting the props to be recommended;
acquiring recommendation information corresponding to a plurality of props to be recommended;
and outputting the recommendation information according to the ordering of the plurality of props to be recommended.
In addition, in order to achieve the above object, the present invention also provides a recommendation device for game props, the recommendation device for game props comprising: the method comprises the steps of a memory, a processor and a recommendation program of the game props, wherein the recommendation program of the game props is stored in the memory and can be run on the processor, and the recommendation program of the game props is executed by the processor to realize the recommendation method of the game props.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a recommended program of a game prop, which when executed by a processor, implements the steps of the method for recommending a game prop according to any one of the above.
According to the game prop recommending method, recommending device and computer readable storage medium, a current game scene is obtained, characteristic data of a user are obtained, a prop to be recommended is determined according to the current game scene and the characteristic data, and recommending information is output according to the prop to be recommended. According to the invention, the current game scene and the user characteristic data are determined, the prop information corresponding to the current game scene and the user characteristic data is output, and the recommendation of the game prop can be changed according to different game scenes and users, so that the recommendation of the game prop is more accurate.
Drawings
FIG. 1 is a schematic diagram of a terminal structure of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for recommending game props according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a refinement flow chart of step S20 in FIG. 2 according to the present invention;
FIG. 4 is a schematic diagram of a refinement flow chart of step S30 in FIG. 2 according to the present invention;
fig. 5 is a flowchart of a method for recommending game props according to a fourth embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The main solutions of the embodiments of the present invention are:
acquiring a current game scene;
acquiring characteristic data of a user, wherein the characteristic data is obtained according to game data of the user, and the game data comprises at least one of identity information, game role information and historical behavior records of the user;
determining props to be recommended according to the current game scene and the characteristic data;
and outputting recommendation information according to the prop to be recommended.
In the prior art, in games such as a fighting owner, a plurality of different kinds of game props exist for users to purchase and use, and different special functions are provided for the current game roles of the users. However, current play objects are typically recommended to the user as a fixed play object or objects, resulting in inaccurate recommendations of play objects.
The embodiment of the invention provides a solution, wherein the current game scene and the user characteristic data are determined, the prop information corresponding to the current game scene and the user characteristic data is output, and the recommendation of the game props can be changed according to different game scenes and users, so that the recommendation of the game props is more accurate.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention is terminal equipment such as a PC, a smart phone, a tablet personal computer and the like.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 1 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in FIG. 1, a memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a recommendation for game play items.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and processor 1001 may be configured to invoke the recommended program of the game play object stored in memory 1005 and perform the following operations:
acquiring a current game scene;
acquiring characteristic data of a user, wherein the characteristic data is obtained according to game data of the user, and the game data comprises at least one of identity information, game role information and historical behavior records of the user;
determining props to be recommended according to the current game scene and the characteristic data;
and outputting recommendation information according to the prop to be recommended.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
acquiring interface information of a current game interface;
and determining the current game scene according to the interface information.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
obtaining game data corresponding to the user;
performing dimension reduction processing on the game data to obtain feature vectors;
and obtaining the characteristic data according to the characteristic vector.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
descriptive statistics are performed on the game data to determine missing values and outliers in the game data;
optimizing the game data according to the missing values and the outliers;
and performing dimension reduction processing on the optimized game data to obtain feature vectors.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
acquiring preset characteristic data corresponding to the current game scene;
calculating the similarity between the characteristic data and the preset characteristic data;
determining target feature data in the preset feature data according to the similarity;
and determining the props to be recommended according to the game props corresponding to the target characteristic data.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
acquiring owned props corresponding to the users;
and determining the props to be recommended according to the game props corresponding to the target characteristic data and the owned props.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
when a selection instruction triggered by the recommendation information is detected, determining a game prop corresponding to the selection instruction;
and updating the preset characteristic data according to the game props corresponding to the selection instruction and the characteristic data.
Further, the processor 1001 may call a recommendation program of the game props stored in the memory 1005, and further perform the following operations:
when a plurality of props to be recommended exist, sorting the props to be recommended;
acquiring recommendation information corresponding to a plurality of props to be recommended;
and outputting the recommendation information according to the ordering of the plurality of props to be recommended.
Referring to fig. 2, in a first embodiment, the recommendation method for game props includes the steps of:
step S10, acquiring a current game scene;
in this embodiment, when a user runs a game through a terminal device, a corresponding current game scene is acquired. The current game scene is determined according to the progress of the current game. For example, when a user logs in to a game, the current game scene is a login scene, when the user starts a game play, the current game scene is a play start scene, when the user ends the current game play, the current game scene is a play end scene, and when the user opens an in-game store, the current game scene is a game consumption scene.
Optionally, acquiring interface information of a current game interface of the terminal equipment, and determining a current game scene according to the interface information. For example, when the current game interface is determined to be a login interface according to the interface information of the current game interface, it is indicated that the user is logging in the game, and thus the current game scene is a login scene, and when the current game interface is determined to be a game play start interface according to the interface information of the current game interface, it is indicated that the user is in a game play, and thus the current game scene is a play start scene. The current game scene is determined according to the interface information, and may be determined according to characters, images, and other features in the interface information, for example, the current game scene is determined according to characters "login", "start", and the like in the interface information.
Step S20, acquiring the characteristic data of the user,
the characteristic data are obtained according to game data of the user, wherein the game data comprise at least one of identity information, game role information and historical behavior records of the user;
in this embodiment, when a user runs a game through a terminal device, feature data of the user is acquired. The characteristic data of the user is obtained according to the game data of the user.
The game data includes at least one of identity information of the user, game character information, and a user history behavior record. The identity information of the user may include a source of downloading game software by the user, a user account type, a user gender, a user age, a terminal type of a game played by the user, a terminal brand, a terminal model, a terminal price, a location where the user logs in to the game, a user membership grade, and the like. The game character information includes a game character class, game equipment, and the like. The user history behavior record may include a user's history of recharging payment records, a prop purchase record, and the like. The game data may also include a charged amount corresponding to the user, a consumed amount corresponding to the user, a user gameplay preference, a number of transactions of game props and game gear, a number of game props used, a user game prop used preference, whether a game login time point is a weekend, whether a login time point is a holiday, and the like.
Step S30, determining props to be recommended according to the current game scene and the characteristic data;
and S40, outputting recommendation information according to the prop to be recommended.
In this embodiment, after the feature data of the current game scene and the user are obtained, the prop to be recommended is determined according to the current game scene and the feature data. For example, the corresponding prop category can be determined according to the current game scene and the characteristic data of the user, and then the prop of the prop category is used as the prop to be recommended, wherein the current game scene, the characteristic data and the prop category are in one-to-one correspondence. Of course, prop categories corresponding to the current game scene and the characteristic data of the user may also be determined by a cluster analysis class classification algorithm.
After determining the prop to be recommended, acquiring recommendation information corresponding to the prop to be recommended, and outputting the recommendation information corresponding to the prop to be recommended in a display interface of the terminal equipment so as to recommend the prop to be recommended to a user.
Optionally, when there are multiple items to be recommended at the same time, recommendation information corresponding to the multiple items to be recommended may be obtained, and the multiple items to be recommended are ordered according to a preset rule, and then the preset number of items to be recommended, which are ranked in front in the order, are used as the current recommended items recommended to the user, and recommendation information corresponding to the multiple current recommended items are sequentially output in a display interface of the terminal device according to the order, so that the user can conveniently select the game items.
In the technical scheme disclosed by the embodiment, the current game scene and the user characteristic data are determined, the prop information corresponding to the current game scene and the user characteristic data is output, and the recommendation of the game props can be changed according to different game scenes and users, so that the recommendation of the game props is more accurate.
In the second embodiment, as shown in fig. 3, on the basis of the embodiment shown in fig. 2, step S20 includes:
step S21, obtaining game data corresponding to the user;
s22, performing dimension reduction processing on the game data to obtain feature vectors;
in this embodiment, the game data corresponding to the user includes at least one of identity information, game role information, and historical behavior records of the user, and may further include a charged amount corresponding to the user, a consumed amount corresponding to the user, a game play preference of the user, a number of transactions between the game prop and the game device, a number of game prop uses, a user game prop use preference, whether a game login time point is a weekend, whether a login time point is a holiday, and the like.
After the game data is obtained, performing dimension reduction processing on the game data, and converting the high-dimension data into low-dimension data to obtain the feature vector after dimension reduction. The dimension reduction processing can be realized by a principal component analysis algorithm (Principal Component Analysis, PCA), truncated singular value decomposition (Truncated singular value decomposition, TSVD) and the like.
The truncated singular value decomposition is a matrix factorization technology, wherein a matrix A is decomposed into U, sigma and V, and a decomposition matrix with a specified dimension is generated by calculating the maximum K singular values specified by a user, so that dimension reduction of game data is realized, and the universality rule of the game data is obtained.
Optionally, when performing the dimension reduction processing on the game data, the acquired game data is first subjected to the optimization processing. For example, descriptive statistics is performed on the acquired game data, missing values and abnormal values in the game data are found through the descriptive statistics, then the missing values are supplemented by adjacent data of the missing values, abnormal values are clear, optimization of the game data is achieved, and further dimension reduction processing is performed on the optimized game data. The descriptive statistics are statistical descriptions of related data of all variables of the investigation population, and mainly comprise frequency analysis of the data, centralized trend analysis of the data, discrete degree analysis of the data, distribution of the data and basic statistical figures. Common indicators include mean, median, mode, variance, standard deviation, etc. The central tendency of data is generally represented by average value and median. The degree of dispersion of the data is generally expressed in terms of variance and standard deviation.
And S23, obtaining the characteristic data according to the characteristic vector.
In this embodiment, after the feature vector is obtained, the feature data corresponding to the user is obtained according to the feature vector. Specifically, after all the game data of the user are subjected to one-time dimension reduction, the feature vector obtained through dimension reduction is used as the feature data of the user, or after the game data of the user are divided into a plurality of parts and each part of the game data are subjected to dimension reduction, the plurality of feature vectors obtained through dimension reduction are integrated, and the integrated feature vector is used as the feature data of the user.
In the technical scheme disclosed by the embodiment, the game data corresponding to the user is subjected to dimension reduction processing to obtain the feature vector, so that the feature data of the user is obtained, and the purpose of extracting the user features of the game is realized.
In a third embodiment, as shown in fig. 4, on the basis of any one of the embodiments shown in fig. 2 to 3, step S30 includes:
step S31, obtaining preset characteristic data corresponding to the current game scene;
step S32, calculating the similarity between the characteristic data and the preset characteristic data;
in this embodiment, after the feature data of the current game scene and the user are obtained, the preset feature data corresponding to the current game scene is obtained. Each game scene can correspond to a plurality of different preset characteristic data, and each preset characteristic data corresponds to one or a plurality of props to be recommended.
Optionally, calculating the similarity between the feature data and the preset feature data, and determining similar preset feature data corresponding to the feature data of the user according to the similarity. The current user can be regarded as the user corresponding to the similar preset feature data belongs to the same type of user, and further the prop to be recommended corresponding to the similar preset feature data is taken as the prop to be recommended corresponding to the current user.
Alternatively, the similarity of the feature data with the preset feature data may be obtained by at least one of euclidean distance, manhattan distance, and mahalanobis distance. For example, the euclidean distance between the feature vector in the feature data and the preset feature vector in the preset feature data is calculated, and the calculated euclidean distance is used as the similarity.
Step S33, determining target feature data in the preset feature data according to the similarity;
in this embodiment, after calculating the similarity between the feature data of the user and each preset feature data, comparing the multiple similarities to determine the target feature data in the preset feature data, where the user corresponding to the target feature data and the user corresponding to the feature data are characterized as belonging to the same class of users.
Optionally, after comparing the plurality of similarities, the preset feature data having the highest similarity with the feature data is taken as the target feature data.
Optionally, the target feature data is determined using a k-Nearest Neighbor (KNN) algorithm. Specifically, after comparing the plurality of similarities, a plurality of preset feature data having a higher similarity with the feature data are taken as target feature data, that is, a plurality of target feature data are determined simultaneously.
And step S34, determining the props to be recommended according to the game props corresponding to the target characteristic data.
In this embodiment, after determining the target feature data, the game prop corresponding to the target feature data is used as the prop to be recommended. When a plurality of target feature data exist at the same time, game props corresponding to the plurality of target feature data are obtained, the occurrence frequency of each game prop in the game props corresponding to the plurality of target feature data is counted, and the game props with the larger occurrence frequency and the preset number are used as props to be recommended.
Optionally, after the game prop corresponding to the target feature data is obtained, the owned prop corresponding to the user can also be obtained, and because a certain prop is owned in the game account corresponding to the user, the user does not need to purchase the prop again, so when determining the prop to be recommended, the owned prop of the user is filtered out from the game prop corresponding to the target feature data, and further, the prop to be recommended is determined according to the game prop corresponding to the filtered target feature data, and invalid repeated recommendation of the game prop is avoided.
In the technical scheme disclosed by the embodiment, the target feature data of the preset feature data is determined by calculating the similarity between the feature data of the user and the preset feature data, and the aim of determining the prop to be recommended corresponding to the user is fulfilled by taking the game prop corresponding to the target feature data as the prop to be recommended.
In the fourth embodiment, as shown in fig. 5, after step S40, on the basis of any one of the embodiments shown in fig. 2 to 4, the method further includes:
step S50, when a selection instruction triggered by the recommendation information is detected, determining a game prop corresponding to the selection instruction;
in this embodiment, after the recommended information is output on the display interface of the terminal device according to the prop to be recommended, if a selection instruction triggered by the user through the recommended information is detected, determining the game prop corresponding to the selection instruction. Optionally, the user may trigger a plurality of selection instructions through the recommendation information to simultaneously select a plurality of props to be recommended.
And step S60, updating the preset characteristic data according to the game props corresponding to the selection instruction and the characteristic data.
In this embodiment, the game props corresponding to the selection instruction represent the preference of the current user for the game props, so that the game props corresponding to the selection instruction and the feature data of the user can be stored in a local server or terminal device in an associated manner, so as to be used as newly added preset feature data and the corresponding game props, and update of the preset feature data is realized. And continuously updating the preset characteristic data through feedback of the user so as to improve the accuracy of the game props recommended to the user.
In the technical scheme disclosed by the embodiment, the preset characteristic data is updated according to the game props and the characteristic data corresponding to the user selection instruction, and the preset characteristic data is updated through feedback of the user, so that the accuracy of game prop recommendation is improved.
In addition, the embodiment of the invention also provides a recommendation device for the game props, which comprises the following components: the method comprises the steps of realizing the method for recommending the game props according to each embodiment when the recommended program of the game props is executed by the processor.
In addition, the embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a recommendation program of the game props, and the recommendation program of the game props realizes the steps of the recommendation method of the game props in each embodiment when being executed by a processor.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (6)

1. The recommendation method of the game props is characterized by comprising the following steps of:
acquiring a current game scene;
obtaining game data corresponding to a user, and dividing the game data into a plurality of sub-data, wherein the game data comprises at least one of identity information, game role information and user history behavior records of the user;
descriptive statistics is carried out on each part of sub data respectively so as to determine missing values and abnormal values in each part of sub data;
optimizing corresponding sub-data according to the missing value and the abnormal value;
performing dimension reduction processing on the optimized sub-data according to a truncated singular value decomposition method to obtain a characteristic sub-vector;
integrating the feature sub-vectors obtained according to each part of sub-data to obtain the feature data of the user;
determining props to be recommended according to the current game scene and the characteristic data;
outputting recommendation information according to the prop to be recommended;
the step of determining the prop to be recommended according to the current game scene and the characteristic data comprises the following steps:
acquiring preset characteristic data corresponding to the current game scene;
calculating Euclidean distance between the feature vector in the feature data and a preset feature vector in the preset feature, and determining similarity between the feature data and the preset feature data according to the Euclidean distance;
taking a plurality of preset feature data with higher similarity with the feature data as target feature data;
and acquiring the owned props corresponding to the users, corresponding game props corresponding to the owned props in the target feature data, and determining the props to be recommended according to the filtered target feature data.
2. The method of recommending game play objects of claim 1, wherein the step of obtaining a current game scene comprises:
acquiring interface information of a current game interface;
and determining the current game scene according to the interface information.
3. The method of recommending game play objects according to claim 1, further comprising, after the step of outputting recommendation information according to the object to be recommended:
when a selection instruction triggered by the recommendation information is detected, determining a game prop corresponding to the selection instruction;
and updating the preset characteristic data according to the game props corresponding to the selection instruction and the characteristic data.
4. The recommendation method of game props according to claim 1, wherein the step of outputting recommendation information according to the props to be recommended comprises:
when a plurality of props to be recommended exist, sorting the props to be recommended;
acquiring recommendation information corresponding to a plurality of props to be recommended;
and outputting the recommendation information according to the ordering of the plurality of props to be recommended.
5. A recommendation device for a game prop, the recommendation device for a game prop comprising: memory, a processor and a recommendation program for game play objects stored on the memory and executable on the processor, which recommendation program for game play objects, when executed by the processor, implements the steps of the recommendation method for game play objects according to any of claims 1 to 4.
6. A computer-readable storage medium, wherein a recommended program of game props is stored on the computer-readable storage medium, which when executed by a processor, implements the steps of the recommendation method of game props according to any one of claims 1 to 4.
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