A game user data management system based on big data

Abstract

The present invention belongs to the technical field of game user management, and specifically discloses a game user data management system based on big data, including a user performance data extraction module, a game performance scoring module, a social performance scoring module, a user label classification module, a game playability tendency label parsing module, and a game recommendation module. By retrieving the game performance data and social performance data of users in the game platform to analyze their game performance and social performance, the users are classified and labeled. At the same time, a list of games played by users with the same classification label is compiled, and then the playability tendency label of the games is parsed based on the user's classification label. This multi-dimensional data-driven method can screen out similar games that highly match the user's classification label from the game list for recommendation, significantly improving the adaptability and accuracy of game recommendations, thereby optimizing the user experience and reducing user churn.

Description

Technical Field

[0001] The present invention belongs to the field of game user management technology, and in particular relates to game user classification recommendation technology, and specifically discloses a game user data management system based on big data. Background Art

[0002] The fast pace and multiple pressures of modern society have made gaming a popular way for many people to relieve stress and find pleasure. As their user base expands, gaming platforms are rapidly developing. To improve user retention, platforms employ personalized recommendation systems. This system analyzes user preferences based on user data on the gaming platform to provide targeted game recommendations, enhancing user engagement and satisfaction.

[0003] Prior art solutions for recommending games based on user classification exist. For example, Chinese invention patent publication number CN118172131A discloses a method, apparatus, device, and storage medium for recommending information based on user behavior. This method records detected operational behavior data and associated scenario information during game play, and then determines a user preference index corresponding to different scenario information. Game recommendation information is then determined based on this user preference index, and information recommendations are made. In this solution, the analysis of user preferences primarily relies on the user's operational behavior data during game play, specifically, their in-game performance. While this analysis method can capture a user's gameplay style and interests, it ignores their social performance data, resulting in a somewhat one-sided preference analysis. Specifically, if a game's content meets a user's preferences but the game has a strong social component, recommending that game to a user with weak social performance may result in poor compatibility, negatively impacting the user experience and increasing the risk of user churn.

[0004] Another example is a Chinese invention patent with publication number CN109146627A, which discloses a game recommendation method. By obtaining user information (such as a list of installed games, game usage time, address book friend list, etc.), users are clustered into different clusters, and a corresponding game list is generated to recommend unregistered games to the user. This solution can combine the user's game usage data and social data for cluster analysis, thereby improving the targeted nature of the recommendation. However, this method has a limitation when recommending unregistered games: it fails to fully consider the consistency between the games in the game list and the user's cluster. Specifically, when a user registers a game, since they cannot know the clusters of other users in the game, the game list will contain some games that are inconsistent with the characteristics of the cluster to which the user belongs. If these games are not further matched and screened, the adaptability and accuracy of the recommendation system will be directly affected. Summary of the Invention

[0005] In view of the shortcomings of the existing technology mentioned above, the present invention proposes a game user data management system based on big data. After comprehensive classification based on the game performance data and social performance data of users in the game, similar games are matched and screened from the game lists of classified users for recommendation, which effectively makes up for the problems mentioned in the background technology.

[0006] The objectives of the present invention can be achieved through the following technical solutions: A game user data management system based on big data, including: a user performance data extraction module, which is used to count the total number of registered users on the game platform, and extract the user's game behavior records and social behavior records in each time period according to a set time gradient period, and extract game performance data and social performance data from them.

[0007] The game performance scoring module is used to construct a game performance matrix based on the user's game performance data in each time period, thereby analyzing the user's game performance activity and game performance activity trend in each time period, and then scoring the user's game performance accordingly.

[0008] The social expression scoring module is used to construct a social expression matrix based on the user's social expression data in each time period, thereby analyzing the user's social expression activity and social expression activity trend in each time period, and then scoring the user's social expression accordingly.

[0009] The user tag classification module is used to classify users by tags based on their gaming performance and social performance.

[0010] The game playability tendency tag parsing module is used to retrieve the game list of users with the same classification tag and summarize it, and count the number of registered users of each game. At the same time, it parses the game playability tendency tag based on the classification tag of the registered users.

[0011] The game recommendation module is used to match the user's classification label with the playability label of each game in the summary game list to make game recommendations.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention analyzes the game performance and social performance data of users in the game platform, thereby classifying and labeling users, and at the same time compiling a list of games played by users with the same classification label, and then analyzing the playability labels of games based on the user's classification label. This multi-dimensional data-driven method can filter out similar games that are highly matched with the user's classification label from the game list for recommendation, significantly improving the adaptability and accuracy of game recommendations, thereby optimizing the user experience and reducing user churn.

[0013] (2) Before selecting similar games from the list of games played by users with the same classification tag for recommendation, the present invention adds an analysis of the distribution statistics of the new and old user groups of the selected games and the groups with a tendency to be lacking, and further selects games with a relatively lacking new user group for recommendation to users, which can effectively optimize the balance of the gaming ecosystem. By recommending games with few new users but high potential, the platform can attract more new users to these games, promote the diversification and healthy development of the gaming community, and thus enhance the vitality of the entire gaming ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention.

[0016] Figure 2 This is a schematic diagram of the game performance data composition in the present invention.

[0017] Figure 3 This is a schematic diagram of the social performance data composition in the present invention.

[0018] Figure 4 Several category diagrams are generated for cross-classifying the gaming performance level and the social performance level in the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Reference Figure 1 As shown, the present invention proposes a game user data management system based on big data, including a user performance data extraction module, a game performance scoring module, a social performance scoring module, a user label classification module, a game playability tendency label parsing module, and a game recommendation module, wherein the user performance data extraction module is connected to the game performance scoring module and the social performance scoring module respectively, the game performance scoring module and the social performance scoring module are both connected to the user label classification module, the user label classification module is connected to the game playability tendency label parsing module, and the user label classification module and the game playability tendency label parsing module are both connected to the game recommendation module.

[0021] The user performance data extraction module is used to count the total number of registered users on the game platform and obtain the user's registered account. At the same time, it extracts the user's game behavior records and social behavior records in each time period according to a set time gradient period, and extracts game performance data from the game behavior records and social performance data from the social behavior records.

[0022] In the example applied to the above scheme, the set time gradient period can be 30 days, 90 days, 6 months, 12 months, etc. By setting the time gradient period, the dynamic changes of the user's performance behavior in the game platform can be captured. Specifically, by setting a shorter time gradient period (such as 30 days), the user's short-term performance behavior can be observed. By setting a medium-length time gradient period (such as 90 days, 6 months), the medium- and long-term performance behavior can be observed. By setting a longer time gradient period (such as 12 months), the user's long-term performance behavior can be observed. In this way, the changing trend of the user's performance behavior can be captured through the time gradient performance behavior. Compared with simply analyzing the user's behavior performance based on a fixed time period, the setting of multiple time gradient periods can dynamically and reasonably analyze the user's gaming performance and social performance, which is conducive to improving the accuracy and real-time performance of the analysis, and also provides the platform with richer user behavior insights.

[0023] In the supplementary explanation to the above solution, game behavior records are automatically generated and recorded by the platform each time a user logs in to the game. These records include, but are not limited to, the user's game time, task progress, level completion, item usage, and other comprehensive game behavior data. These records are stored by the platform and associated with the user's registered account.

[0024] Social behavior records are automatically generated and recorded by the platform every time a user logs into the game platform to engage in social activities. The social behavior records can be extracted from the friend relationship list, instant messaging, team formation, social event participation, and social evaluation. The generated social behavior records will be stored by the platform and associated with the user's registered account.

[0025] In the preferred implementation of the above scheme, see Figure 2 As shown, game performance data includes game duration data, game progress data and item usage data. In a further example, game duration data includes but is not limited to single game duration, game login frequency, longest continuous game duration, etc. Game progress data includes but is not limited to level completion rate, clearance time, clearance rate, etc. Item usage data includes but is not limited to item holdings, item usage frequency, item consumption amount, etc.

[0026] In another preferred embodiment of the above scheme, see Figure 3 As shown, social performance data includes social relationship data, social interaction data and social influence data, among which social relationship data reflects the social network structure of users in the game. Specifically, social relationship data includes but is not limited to the number of friend lists, the frequency of adding friends, the duration of friend relationships, etc. Social interaction data reflects the specific social interaction behavior of users in the game. Specifically, social interaction data includes but is not limited to the length of interactive messages, the frequency of interactive messages, the number of consecutive interactive days, etc. Social influence data reflects the influence of users in the game community. Specifically, social influence data includes but is not limited to the fan growth rate, the frequency of content release, the success rate of game recommendations, etc.

[0027] The data extracted by the present invention when retrieving the game behavior records and social behavior records of users on the game platform to extract game performance data and social performance data includes multiple aspects, which can characterize game performance and social performance in multiple dimensions, and provide solid data support for subsequent game performance and social performance scoring.

[0028] It should be noted that due to the inconsistency of the duration scales of different time gradient cycles, the number of game behavior records and social behavior records retrieved in each time period may be different. Therefore, when extracting game performance data and social performance data, some data in different game behavior records and social behavior records within the corresponding period can be averaged to eliminate the impact of time scale differences and improve the accuracy of data analysis. For example, when extracting a single game duration, each game behavior record has a game duration. The game duration in each game behavior record can be averaged to obtain the single game duration. However, for certain specific types of data, such as clearance rate, friend addition frequency, longest continuous game duration, etc., there is no need to perform average calculation. The reason is that taking the average may obscure its intrinsic meaning or lose key information, so it should be kept as it is.

[0029] The game performance scoring module is used to form a game performance matrix with the game performance data of the user in each time period, thereby analyzing the user's game performance activity and game performance activity trend in each time period, and then scoring the user's game performance accordingly.

[0030] Preferably, the game performance data of the user in each time period is constructed into a game performance matrix as follows: define the time period as the row of the matrix, define the game performance data as the column of the matrix, construct the game performance matrix, and use the user's registered account as the matrix identifier.

[0031] In the above preferred implementation example, the game performance matrix is constructed as In this matrix, T1, T2, T3, and T4 represent the time periods within the time gradient cycle, respectively.GD 、data GP 、data PU They represent game duration data, game progress data, and item usage data respectively.

[0032] The user's game performance data in each time period is extracted, normalized, and then filled into the constructed game performance matrix.

[0033] It should be noted that the normalization process mentioned above is to ensure that the numerical ranges of performance data of different types of games are consistent and to eliminate the dimensional differences between different indicators. For example, the normalization process can adopt a maximum-minimum normalization method.

[0034] Further preferably, the following process is used to analyze the user's gaming activity in each time period: weights are assigned to the gaming duration, gaming progress, and item usage.

[0035] In the above further preferred example, the weights for game duration, game progression, and item usage can be assigned 30%, 50%, and 20%, respectively. This weighting is based on the fact that game progression is a core indicator for measuring a user's actual progress and depth of engagement in the game. It reflects key behaviors such as completing tasks, clearing levels, and unlocking achievements, directly reflecting their skill level and engagement. Game duration is a key indicator of user activity, reflecting the amount of time and frequency a user spends in the game. Longer game duration generally indicates higher interest, but it can also indicate inefficient play. Therefore, while game duration is an important indicator of activity, it does not fully represent the actual quality of user engagement. Item usage reflects the user's consumption and utilization of virtual items in the game and is a key indicator of user economic behavior and strategic choices. The frequency and type of item usage can reflect a user's spending habits and gaming strategies. However, the frequency and amount of item usage can vary significantly across users, and not all users use items frequently. Therefore, item usage can only serve as a supplementary indicator of game activity and is far less important than game duration and game progression.

[0036] Focus the user's game performance matrix on the columns corresponding to the time period of each row, and then extract the data in the corresponding columns and combine them with the weighted values of game time, game progress, and props used to perform weighted average calculation to obtain the user's game performance activity in each time period.

[0037] It should be added that when using game time data, game progress data and prop usage data to calculate the weighted average of game performance activity, since these data also contain different types of data, it is recommended to first normalize the internal data to ensure that data of different categories are comparable. Subsequently, a weighted average calculation is performed based on the normalized data to obtain the score values of game time, game progress and prop usage. The score values of game time, game progress and prop usage are then combined with the weight values assigned to game time, game progress and prop usage to perform a weighted average calculation to finally obtain a comprehensive score of game performance activity.

[0038] More preferably, the game performance activity trend is analyzed as follows: each time period in the time gradient cycle is numbered in order from far to near in time.

[0039] In the above further preferred example, the 30 days, 90 days, 6 months, and 12 months in the time gradient period are arranged in order from far to near as 12 months, 6 months, 90 days, and 30 days.

[0040] According to the arrangement number of the time periods, the game performance activity of each time period is subtracted from the game performance activity of the previous time period and then divided by the interval length between the two time periods to obtain the game performance activity change rate of each time period.

[0041] It should be noted that since the first time period has no previous time period, it starts from the second time period.

[0042] The game performance activity change rate of each time period is calculated using the smoothing formula S t =α*S 0t +(1-α)*S t-1 Get the smoothed game activity change rate S for each time period t , where t represents the time period number, t=1,2,......,m, S 0t represents the game performance activity change rate in time period t, S t-1 represents the smoothed game activity change rate in the t-1th time period, α represents the smoothing factor, and 0<α<1, which determines the weight of recent data. Generally, a larger α value will make the smoothed result closer to the recent data, while a smaller α will make the smoothed result more stable. For example, α=0.6.

[0043] It should be understood that after obtaining the game performance activity change rate for each time period, the present invention does not directly use these raw data to perform game performance activity trend analysis, but first smoothes the activity change rate for each time period to eliminate the impact of short-term fluctuations, thereby obtaining a more stable activity change rate. This helps to more accurately reflect the long-term behavioral trends of users. Specifically, the present invention adopts an exponentially weighted moving average method, which achieves smoothing by assigning higher weights to recent time periods, so that recent data has a greater impact on the smoothing result, while the impact of data from earlier time periods gradually weakens, so that short-term random fluctuations can be smoothed while retaining recent trends.

[0044] The smoothed game activity change rate for each time period is cumulatively averaged to obtain the user's corresponding game performance activity trend.

[0045] It should be added that the game performance activity trend value can be positive or negative. When the game performance activity trend is positive, it indicates that the user's overall game activity is increasing. When the game performance activity trend is negative, it indicates that the user's overall game activity is decreasing.

[0046] Further preferably, the scoring of the user's gaming performance is implemented as follows: the user's gaming performance activity in each time period is combined with the gaming performance activity trend into the scoring formula Get the user's game performance GP, where represents the average gaming activity of the user in each time period, k represents the gaming activity trend of the user, Q′ represents the gaming activity of the user in the most recent time period, e represents a natural constant, d represents a rating system, for example, d can be a one-point system, a ten-point system, or a percentage system, when d is a one-point system, d=1, when d is a percentage system, d=100, η represents the fluctuation index of the gaming activity of the user in each time period, where max{Q t}、min{Q t} respectively represent the maximum game performance activity and the minimum game performance activity of the user in each time period, and η0 represents the limited fluctuation index preset by the system. For example, η0=0.5.

[0047] It should be understood that the aforementioned scoring of gaming performance utilizes both static and dynamic dimensions, combining a user's gaming activity and gaming activity trends across various time periods. The effective representative selected from the gaming activity for each time period reflects the user's static gaming performance, while the gaming activity trend reflects the user's dynamic gaming performance. This combined static and dynamic approach results in a more comprehensive and accurate gaming performance score. Furthermore, when selecting the effective static representative, the user's average gaming activity is not directly selected. Instead, the selection is based on the fluctuation in the user's gaming activity across various time periods. If a user's gaming activity fluctuates minimally across various time periods, indicating relatively stable activity, the user's average gaming activity is selected as the effective static representative. This reflects the user's overall activity level within that time period and avoids bias caused by individual outliers. If a user's gaming activity fluctuates significantly across various time periods, indicating significant instability, the user's gaming activity from the most recent time period is selected as the effective static representative. This better captures the user's current true activity status and avoids the impact of abnormal fluctuations in historical data on the evaluation results.

[0048] The social expression scoring module is used to construct a social expression matrix from the social expression data of the user in each time period, thereby analyzing the social expression activity and social expression activity trend of the user in each time period, and then scoring the social expression of the user accordingly.

[0049] The above steps of constructing a social performance matrix, analyzing the user's social performance activity and social performance activity trends in each time period, and scoring the user's social expressiveness can be similarly referred to the analysis process of game performance.

[0050] The user label classification module is used to label and classify users according to their gaming performance and social performance. The specific classification is as follows: based on the value range of gaming performance and social performance, different gaming performance levels and social performance levels are defined, and the gaming performance division interval corresponding to each gaming performance level and the social performance division interval corresponding to each social performance level are recorded.

[0051] In the above exemplary implementation, in the expressiveness scoring, when the scoring system is a one-point system, the value range of expressiveness is 0 to 1; when the scoring system is a ten-point system, the value range of expressiveness is 0 to 10; when the scoring system is a hundred-point system, the value range of expressiveness is 0 to 100.

[0052] Furthermore, in the above exemplary implementation, different levels of gaming performance and social performance can be defined as two, three, four, or five levels. Under a two-level range of 0 to 100, the gaming performance level and its corresponding gaming performance ranges are: high gaming performance: 60-100, low gaming performance: 0-59. The same applies to social performance levels and their corresponding social performance ranges.

[0053] The game performance and social performance of each user on the game platform are compared with the game performance division intervals corresponding to each game performance level and the social performance division intervals corresponding to each social performance level to obtain the game performance level and social performance level corresponding to each user.

[0054] The degree of gaming performance and the degree of social performance were cross-classified to generate several categories.

[0055] In a further exemplary implementation, the cross-classification of the two levels of gaming performance and social performance results in four categories, see Figure 4 As shown, the first category can represent high gaming performance and high social performance, the second category can represent high gaming performance and low social performance, the third category can represent low gaming performance and high social performance, and the fourth category can represent low gaming performance and low social performance.

[0056] Users are matched to corresponding categories based on their gaming performance and social performance, thereby assigning a classification label to each user.

[0057] The game playability tendency tag parsing module is used to retrieve and summarize the game lists of users with the same classification tag, and count the number of registered users of each game, and at the same time parse the game playability tendency tags based on the classification tags of the registered users;

[0058] It should be noted that deduplication is required when aggregating game lists for users with the same category label.

[0059] Preferably, parsing the game's playability tendency tags is implemented as follows: categorizing the classification tags of all registered users in each game according to the same classification tag, thereby summarizing the occurrence ratio of each classification tag among the game's registered users.

[0060] Statistics are collected on the cumulative game time of users registered under each category tag, and the contribution factor of each category tag is calculated based on the cumulative game time.

[0061] It should be emphasized that the cumulative game time mentioned above does not only refer to the time users spend on the game, but also includes the time invested in the game platform including social interaction. It reflects the degree of user investment in the game platform and can reflect the contribution of different classification tags. Specifically, the contribution factor of each classification tag can be calculated by dividing the cumulative game time of registered users under each classification tag by the total cumulative game time of registered users under all classification tags.

[0062] The value of each category label is obtained by multiplying the occurrence ratio of each category label corresponding to the game by the contribution factor, and then the category label corresponding to the maximum value is taken as the game's playability tendency label.

[0063] It should be explained that the playability label reflects the classification label that best represents the characteristics of the game user group.

[0064] The game recommendation module is used to match the user's classification label with the playability tendency label of each game in the summarized game list, thereby making game recommendations. The specific implementation is as follows: the user's classification label is matched with the playability tendency label of each game in the summarized game list, and the successfully matched games constitute the user's corresponding matching game set.

[0065] Obtain the game names registered by each registered user in the game platform, remove the registered game names from the matching game set corresponding to the corresponding user, and record the matching game set after removal as the valid game set.

[0066] The distribution statistics of new and old user groups of games in the effective game collection are carried out to identify the groups with lack of tendency, and then games for new users in the groups with lack of tendency are selected to recommend to users.

[0067] Preferably, in the above solution, the identification of the group lacking tendency refers to the following process: the online time of each game in the valid game set is obtained respectively, and the statistical time range is formed in combination with the current time.

[0068] Determine the demarcation time within the statistical time range, and then compare the registration time of each registered user of each game with the demarcation time, thereby determining the new and old user classification corresponding to each registered user, where users before the demarcation time are old users, and users after the demarcation time are new users.

[0069] For example, the demarcation time can be selected as one year after the game is launched.

[0070] The ratio of new and old users corresponding to each game is counted, and the ratio of new and old users is compared to obtain the game's lack of tendency group. Specifically, when the ratio of old users is higher than the ratio of new users, the game's lack of tendency group is new users; when the ratio of new users is higher than the ratio of old users, the game's lack of tendency group is old users.

[0071] It should be added that the group with a tendency to lack, obtained based on the ratio of new and old users corresponding to the game, reflects the user development direction of the game. By recommending games with a user development direction of new users to users, the platform can help these games attract more new users, improve user composition, and achieve ecological balance. In addition, the continuous influx of new users helps to extend the life cycle of the game and avoid the decline of the game due to user loss. By recommending games with a relatively lacking new user groups, the platform can inject new vitality into these games and maintain their long-term appeal and development potential.

[0072] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A game user data management system based on big data, characterized by ,include: The user performance data extraction module is used to count the total number of registered users on the game platform, and extract the game behavior records and social behavior records of users in each time period according to the set time gradient period, and extract the game performance data and social performance data from them; The gaming performance scoring module is used to compile the user's gaming performance data in each time period into a gaming performance matrix, thereby analyzing the user's gaming performance activity and gaming performance activity trends in each time period, and then scoring the user's gaming performance accordingly; The scoring of the user's gaming performance is implemented as follows: The user's gaming activity in each time period is combined with the gaming activity trend to import into the scoring formula Get the user's gaming performance , where Indicates the average gaming activity of users in each time period. Indicates the user's gaming activity trend. Indicates the user's gaming activity in the recent period. represents a natural constant, Indicates the rating system. Indicates the fluctuation index of the user's gaming activity in each time period, where , 、 Respectively represent the maximum game performance activity and the minimum game performance activity of the user in each time period. Indicates the limited volatility index preset by the system; The social expression scoring module is used to construct a social expression matrix based on the user's social expression data in each time period, thereby analyzing the user's social expression activity and social expression activity trend in each time period, and then scoring the user's social expression accordingly; User label classification module, used to classify users according to their gaming performance and social performance; The game playability tag parsing module is used to retrieve and summarize the game lists of users with the same classification tag, and count the number of registered users for each game. At the same time, it analyzes the game playability tags based on the classification tags of registered users; The game recommendation module is used to match the user's classification label with the playability label of each game in the summary game list to make game recommendations.

2. A game user data management system based on big data according to claim 1, characterized in that: The game performance data includes game duration data, game process data and prop usage data, and the social performance data includes social relationship data, social interaction data and social influence data.

3. A game user data management system based on big data according to claim 2, characterized in that: The process of constructing a game performance matrix from the game performance data of the user in each time period is as follows: Define time periods as matrix rows, define game performance data as matrix columns, construct a game performance matrix, and use the user's registered account as the matrix identifier; The user's game performance data in each time period is extracted, normalized, and then filled into the constructed game performance matrix.

4. A game user data management system based on big data according to claim 3, characterized in that: The analysis of the user's gaming activity in each time period is as follows: Assign weights to game duration, game progress, and item usage; Focus the user's game performance matrix on the columns corresponding to the time period of each row, and then extract the data in the corresponding columns and combine them with the weighted values of game time, game progress, and props used to perform weighted average calculation to obtain the user's game performance activity in each time period.

5. The game user data management system based on big data according to claim 1, characterized in that: The analysis process of the game's active performance trend is as follows: Arrange and number each time period in the time gradient cycle in order from far to near; According to the arrangement number of the time period, the game performance activity of each time period is subtracted from the game performance activity of the previous time period and divided by the interval between the two time periods to obtain the game performance activity change rate of each time period; The game performance activity change rate of each time period is smoothed using the formula Get the smoothed game activity change rate for each time period , where Indicates the time period number. , Indicates the The game performance activity change rate of the time period, Indicates the The game activity change rate after smoothing the time period, represents the smoothing factor, and ; The smoothed game activity change rate for each time period is cumulatively averaged to obtain the user's corresponding game performance activity trend.

6. The game user data management system based on big data according to claim 1, characterized in that: The process of classifying users by labeling them based on their gaming and social performance is as follows: Define different levels of gaming performance and social performance based on their scoring ranges, and record the gaming performance intervals corresponding to each gaming performance level and the social performance intervals corresponding to each social performance level; Comparing the gaming performance and social performance of each user on the gaming platform with the gaming performance division intervals corresponding to each gaming performance level and the social performance division intervals corresponding to each social performance level to obtain the gaming performance level and social performance level corresponding to each user; Cross-classify the degree of gaming performance and the degree of social performance to generate several categories; Users are matched to corresponding categories based on their gaming performance and social performance, thereby assigning a classification label to each user.

7. The game user data management system based on big data according to claim 1, characterized in that: The process of parsing the game playability labels based on the registered user's classification labels is as follows: Classify the category tags of all registered users in each game according to the same category tag, and summarize the occurrence ratio of each category tag among the registered users of the game; Count the cumulative game time of users registered under each category tag, and calculate the contribution factor of each category tag based on the cumulative game time; The value of each category label is obtained by multiplying the occurrence ratio of each category label corresponding to the game by the contribution factor, and then the category label corresponding to the maximum value is taken as the game's playability tendency label.

8. The game user data management system based on big data according to claim 1, characterized in that: The game recommends the following process: Match the user's classification label with the playability label of each game in the summary game list, and obtain the successfully matched games to form the user's corresponding matching game set; Obtain the game names registered by each registered user in the game platform, remove the registered game names from the matching game set corresponding to the corresponding user, and record the matching game set after removal as the valid game set; The distribution statistics of new and old user groups of games in the effective game collection are carried out to identify the groups with lack of tendency, and then games for new users in the groups with lack of tendency are selected to recommend to users.

9. The game user data management system based on big data according to claim 8, characterized in that: The process of collecting statistics on the distribution of new and old user groups of games in the effective game collection and identifying groups lacking in tendency is as follows: Get the launch time of each game in the valid game set respectively, and combine it with the current time to form the statistical time range; Determine the demarcation time within the statistical time range, and then compare the registration time of each registered user of each game with the time, thereby determining the new and old user classification corresponding to each registered user; Count the ratio of new to old users for each game, and compare the two to get the group with a tendency to lack the game.

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