Information processing method and device in game, storage medium, equipment and product

Abstract

The invention discloses an information processing method and device in a game, a storage medium, equipment and a program product, and the method comprises the steps: collecting the related information of various different virtual items in a virtual item game, and providing data support for the subsequent determination of the attraction evaluation of the virtual items; based on the related information of the virtual articles, calculating quantized values of a plurality of preset attraction evaluation indexes related to the attraction of the virtual articles and corresponding to the various virtual articles; based on the quantized values of a plurality of preset attraction evaluation indexes corresponding to each virtual article, respectively determining attraction index values of each virtual article; and finally recommending a target virtual article or a target virtual article combination based on the attraction index values of various virtual articles. According to the method provided by the invention, the attraction of the virtual goods can be evaluated more efficiently and objectively, the virtual goods or the virtual goods combination can be recommended, the experience dependence on a designer is reduced, the virtual goods putting strategy is optimized, and the virtual goods putting efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of games, in particular to a game information processing method and device, a storage medium, equipment and a program product. BACKGROUND

[0002] In games, there is often a problem of virtual item release, such as cards in card games, which include numerical cards, strategy cards, and placement cards, and involve the main cycle of card collection, cultivation, and release. In card games, the design of card release directly affects the revenue and user retention of the game. Annual card release in card games requires designers to rely on experience and professional ability to design and analyze data to determine the attractiveness of different cards, making it easier to select cards with high attractiveness for release, which requires designers to invest a lot of time and effort. Therefore, a more efficient card attractiveness evaluation method is needed to recommend cards or card combinations, reduce reliance on designers, and improve card release efficiency. SUMMARY

[0003] The embodiments of the present application provide a game information processing method, device, storage medium, equipment and program product, which can more efficiently and comprehensively evaluate the attractiveness of virtual items, thereby recommending virtual items or virtual item combinations without designers spending a lot of time and effort to select, reducing the burden on designers.

[0004] In a first aspect, the embodiments of the present application provide a game information processing method, which comprises: Collecting relevant information of various different virtual items; Based on the relevant information, calculating the quantized values of the plurality of preset attractiveness evaluation indexes corresponding to each of the various virtual items; Based on the quantized values of the plurality of preset attractiveness evaluation indexes corresponding to each of the various virtual items, respectively determining the attractiveness index values of the various virtual items; Based on the attractiveness index values of the various virtual items, recommending a target virtual item or a target virtual item combination.

[0005] In a second aspect, the embodiments of the present application provide a game information processing device, which comprises: A collection module for collecting relevant information of various different virtual items; A calculation module for calculating the quantized values of the plurality of preset attractiveness evaluation indexes corresponding to each of the various virtual items based on the relevant information; A determination module for respectively determining the attractiveness index values of the various virtual items based on the quantized values of the plurality of preset attractiveness evaluation indexes corresponding to each of the various virtual items; The recommendation module is used to recommend target virtual items or combinations of target virtual items based on the attractiveness index values ​​of various virtual items.

[0006] Thirdly, according to an embodiment of this application, an electronic device is provided, the electronic device including a processor and a memory, the memory being used to store one or more computer instructions; the processor being used to execute the one or more computer instructions, the processor being used to execute the information processing method in the game as described in any of the above embodiments by calling the one or more computer instructions stored in the memory.

[0007] Fourthly, according to embodiments of this application, a computer-readable storage medium stores a computer program adapted for loading by a processor to execute the information processing method in a game as described in any of the above embodiments.

[0008] Fifthly, according to an embodiment of this application, a computer program product includes computer instructions that, when executed by a processor, implement the information processing method in a game as described in any of the above embodiments.

[0009] This application provides a method, apparatus, storage medium, device, and program product for information processing in games. It collects relevant information about various virtual items to provide data support for subsequent assessment of the attractiveness of these virtual items. Based on this information, it calculates quantitative values ​​for multiple preset attractiveness assessment indicators related to the attractiveness of each virtual item. Then, based on these quantitative values, it determines the attractiveness index value for each virtual item. Finally, based on these attractiveness index values, it recommends target virtual items or combinations of target virtual items. This application, by collecting relevant information about virtual items and calculating multiple preset attractiveness assessment indicators, can more efficiently and objectively assess the attractiveness of virtual items and recommend virtual items or combinations thereof. This reduces reliance on designer experience, optimizes virtual item delivery strategies, and improves delivery efficiency. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a first flowchart illustrating the information processing method in a game provided in an embodiment of this application; Figure 2 This is a second flowchart illustrating the information processing method in a game provided in an embodiment of this application; Figure 3 A schematic diagram of the third process of the information processing method in the game provided in the embodiments of this application; Figure 4 A schematic diagram of the fourth process of the information processing method in the game provided in the embodiments of this application; Figure 5 A fifth flowchart illustrating the information processing method in a game provided in this application embodiment; Figure 6 A schematic diagram of a card distribution workflow for an information processing method in a game provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of the information processing device in the game provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] This application provides an information processing method, apparatus, storage medium, device, and program product for games. For example, the information processing method in the game according to this application can be executed by an electronic device, which can be a terminal or server, etc.

[0014] The terminal can be a smartphone, tablet, laptop, desktop computer, smart TV, smart speaker, wearable smart device, personal computer (PC), smart vehicle terminal, etc. The terminal can also include a client, which can be a video client, shopping application client, reading application client, browser client, or instant messaging client, etc.

[0015] A server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0016] The information processing method in the game provided in this application is specifically illustrated through the following embodiments. These embodiments are described in detail below. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.

[0017] It is understood that in the specific implementation of this application, user object data, context data and other related data are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0018] This application provides an information processing method for games, which can be executed by a terminal or a server, or by both a terminal and a server.

[0019] Please see Figure 1 The information processing method in the game provided in this application embodiment may include the following steps 011, 012, 013 and 014, which are described in detail below.

[0020] Step 011: Collect relevant information about various virtual items.

[0021] Virtual items include cards in card games, equipment in shooting games, and skins in role-playing games.

[0022] The relevant information includes user behavior data, virtual item attribute data, and game economy data. User behavior data includes data on various behaviors performed by players on virtual items. Virtual item attribute data includes data related to the attributes of virtual items (such as basic attributes, effects, and rarity). Game economy data includes data related to the consumption of virtual items.

[0023] Specifically, relevant information is collected through scheduled tasks on the server, involving specific log analysis. Data sources include design configuration data and user behavior logs generated during gameplay.

[0024] User behavior data can be obtained by embedding tracking points at specific locations within the business and generating specific behavior logs when players trigger specific actions.

[0025] Among them, virtual item attribute data, such as card attribute data, includes the card's basic attributes (such as character type, faction, level), rarity, and effects. The card's basic attributes and rarity are design data and can be read through the design configuration. Card effects can be obtained through the effect logs generated during the card's use in the game.

[0026] The acquisition of game economic data is related to users' consumption of in-game currency and game resources under their game accounts. Cards are released through specific channels, and game economic data can be obtained through consumption logs generated by users spending in-game currency through these channels; it can also be obtained through resource investment logs generated by developing specific cards.

[0027] Step 012: Based on relevant information, calculate the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to various virtual items.

[0028] Please see Figure 2 In some embodiments, the relevant information includes user behavior data, virtual item attribute data, and game economy data. User behavior data includes data on various actions performed by players on virtual items. Virtual item attribute data includes data related to virtual item attributes. Game economy data includes data related to the consumption of virtual items. Step 012 includes: Step 0121: Based on user behavior data, virtual item attribute data, and game economy data corresponding to various virtual items, calculate the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to each virtual item.

[0029] It is understandable that the relevant information for different virtual items refers to game data related to the evaluation of the attractiveness of virtual items. This includes behavioral data of users who own virtual items, the attributes and positioning of the virtual items themselves in the game, and data on user spending records related to virtual items. Using this configured design data or user-generated data in the game, the quantitative values ​​of various preset attractiveness evaluation indicators corresponding to each virtual item can be calculated.

[0030] In some embodiments, the preset attractiveness evaluation index includes multiple general attractiveness indices. Virtual items include cards. The general attractiveness indices of cards include at least one of the following: card design rating index, card lineup positioning index, card holding rate index, card development level index, card training material holding rate index, card usage frequency index, card win rate contribution index, and card popularity index. Among them, the card design scoring index is used to evaluate at least one of the following aspects of card design: artistic expression, skill creativity, and balance. Card lineup positioning metrics are used to evaluate at least one of the following: the coreness of a card in a lineup, the versatility of a card, and the difficulty of replacing a card. The card ownership rate metric is used to assess the ownership rate of cards; Card development level indicators are used to assess the difficulty of developing cards. The Card Training Material Holding Rate metric is used to evaluate the holding rate of materials used for training cards; Card usage frequency metrics are used to evaluate the frequency of card usage. The card win rate contribution metric is used to evaluate the win rate when using cards; Card popularity metrics are used to assess the popularity of cards among players.

[0031] It's understandable that card design metrics are indicators that can be initially evaluated before being released into the game, in order to filter out cards with obvious design flaws and identify high-design-score cards with a longer lifespan and collectible value that are not easily depreciated due to version updates.

[0032] Card lineup positioning indicators can filter out the core cards for building a lineup, and can also judge whether the game environment is overly dependent on the card by the difficulty of its substitutability, thereby avoiding health risks.

[0033] Card ownership rate metrics can assess the rarity of cards, directly driving players to acquire high-rarity cards. They can also help determine distribution strategies; for example, duplicate distribution of high-ownership-rate cards should be avoided, while launching exclusive events for low-ownership-rate cards can effectively stimulate consumption.

[0034] The card development level index can reflect the saturation of players' investment and development needs. A higher card development level index means that players have almost exhausted their resource consumption on that card, and their motivation to spend money in the future will weaken. A lower card development level index means that there is greater potential for development needs, and more effort can be put into investing in related development materials.

[0035] The card training material holding rate reflects the difficulty and demand for card development among players. A low card training material holding rate indicates that the card training materials are difficult to obtain. Based on the card holding rate, the distribution of card training materials can be adjusted.

[0036] Card usage frequency reflects the actual popularity of a card. Cards with a high usage frequency indicate strong player acceptance and can be aggressively launched. Cards with a low usage frequency can be considered as easily obtainable cards in events or removed from the store altogether.

[0037] The card win rate contribution metric reflects the core value of a card in a game. It can objectively reflect the degree of influence of a card on winning a game, excluding the influence of other cards or card combinations in the lineup.

[0038] Card popularity metrics are an important indicator for card promotion. The higher the card popularity metric, the greater the attention a card receives within the community. Players can also identify their favorite cards based on the level of discussion, providing a basis for personalized recommendations.

[0039] Specifically, in the process of card attractiveness evaluation, based on the previously collected card information, different numbers (at least one) and types of preset attractiveness evaluation indicators can be set for different cards, and then the quantitative values ​​of each preset attractiveness evaluation indicator can be calculated by comprehensively considering the data in the relevant card information.

[0040] Step 013: Based on the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to each virtual item, determine the attractiveness index value of each virtual item.

[0041] In some embodiments, the quantification value of the general attractiveness index of a virtual item can be determined from information in the relevant information of the corresponding virtual item that matches the general attractiveness index.

[0042] Among the various preset attractiveness assessment indicators, the general attractiveness index for virtual items is one of the most universal, providing a straightforward and intuitive understanding of the attractiveness of virtual items. Before selecting virtual items for deployment, an evaluation can be conducted by combining the specific quantitative values ​​of multiple general attractiveness indicators with the overall attractiveness index value to obtain recommendations and improve deployment efficiency.

[0043] The following explanation and illustration will be based on card games.

[0044] The card's attractiveness index is a value generated by combining multiple preset attractiveness evaluation indicators and quantifying these indicators through a specific calculation. The card's attractiveness index is used to assess its overall performance in the game; the higher the index, the higher the card's overall performance and the greater the likelihood of achieving its intended benefits.

[0045] In some embodiments, the quantitative value of the card design scoring index is determined based on a first preset value corresponding to artistic performance, a second preset value corresponding to skill creativity, and a quantitative value corresponding to balance. The quantitative value corresponding to balance is determined based on at least one of the card's attributes and the card's win rate. The quantitative values ​​of card lineup positioning indicators are determined based on the quantitative values ​​of card coreness, card versatility, and card replacement difficulty. The quantitative value of card coreness is determined based on the proportion of the card as a core role in the target lineup. The quantitative value of card versatility is determined based on the ratio of the number of preset lineups to which the card is applicable to to the total number of lineups. The quantitative value of card replacement difficulty is determined based on the ratio of the number of replaceable cards to the total number of cards of the same quality. The quantitative value of the card holding rate metric is determined based on the ratio of the number of cards held by the target player group to the total number of the target player group; The quantitative value of the card development level indicator is determined based on the ratio of the card's average development level to the card's maximum level, or based on each player's card development level and activity level. The quantitative value of the card training material holding rate indicator is determined based on the ratio of the average number of training materials held by each player to the total amount of training materials required for card development, or based on the ratio of the average number of training materials held by each player to the total amount of training materials required for card development at different development stages. The quantitative value of the card usage frequency index is determined based on the number of times the card is used within a specific time window and the total number of times all cards are used within that time window; The quantitative value of the card win rate contribution indicator is determined based on the difference between the win rate of lineups containing cards and the average win rate of the same type of lineups that do not contain the same cards. The quantitative value of the card popularity index is determined based on the number of times the card is mentioned on social media and official forums, as well as the amount of content created about the card.

[0046] It is understandable that the quantitative values ​​of different preset attractiveness evaluation indicators are obtained by performing certain calculations on the data collected from card-related information.

[0047] For example, the Card Design Score (CDS) can be determined by a combination of the card design's artistic expression, skill creativity, and balance. Artistic expression is evaluated and scored by multiple professional designers during the design phase; skill creativity is evaluated and scored by multiple professional designers based on the card's skill uniqueness and interactivity during the design phase; and balance is obtained through a skill simulation test automatically generated by the system after the card design is completed. The scores for artistic expression, skill creativity, and balance can range from 1 to 10. The quantified value of the Card Design Score can be calculated as: CDS = 0.3 × first preset value corresponding to artistic expression + 0.4 × second preset value corresponding to skill creativity + 0.3 × quantified value of balance. The weights of artistic expression, skill creativity, and balance can be selected and adjusted according to the card's deployment scenario, but the sum of the weights should be 1. The final CDS quantified value is also a score between 1 and 10.

[0048] For example, Card Position Value (CPV) can be determined by a combination of card coreity, card versatility, and card substitutability. Card coreity is determined by the proportion of cards that play a core role in the target's mainstream lineups (i.e., the most popular team compositions with the highest win rates verified by numerous players). Versatility is determined by the ratio of the number of applicable lineups for a card (team compositions that players use and recognize as effective) to the total number of lineups (all team compositions used and recognized by players). Substitutability is determined by the ratio of 1 - the number of substitutable cards (cards of the same quality whose function and role are highly similar to the target card for which the quantification value needs to be determined, and which can be used to perform similar tactical tasks) to the total number of cards of the same quality (quality level, i.e., rarity, which is card design data and can be directly read). The card's core value, versatility, and ease of substitution are all quantified values ​​between 0 and 1, representing collectable card attribute data. The quantified value for a card's lineup positioning indicator can be determined by CPV = 0.5 × card's core value + 0.3 × card's versatility value + 0.2 × card's ease of substitution value. The weights of core value, versatility, and ease of substitution can be selected and adjusted according to the card's deployment scenario; the sum of the weights should be 1, resulting in a final CPV value, also a ratio between 0 and 1.

[0049] For example, the quantifiable value of the Card Ownership Rate (COR) metric is determined by the ratio of the number of cards held by the target player group to the total number of the target player group. For new cards about to be released for the first time, the expected value of the Card Ownership Rate metric can be adjusted based on rarity; this is known as the card's Expected Ownership Rate. The Expected Ownership Rate is related to the release date; higher rarity cards are more valuable, harder to obtain, and have lower expected ownership rates. For example, the expected ownership rate for common cards is 0.9, for rare cards it's 0.9, for epic cards it's 0.2, and for legendary cards it's 0.05.

[0050] For example, the quantification value of the Card Development Level (CDL) metric is determined based on the ratio of the card's average development level to its maximum level, or based on each player's card development level and activity level. There are two main formulas for calculating the quantification value of the Card Development Level metric: Basic formula: CDL = Average Development Level of the Card / Maximum Level of the Card; and Advanced formula: CDL = Σ(Player i's Card Development Level × Player i's Activity Level) / Σ Player Activity Level. The basic formula is primarily used for calculating data over a short time span, assuming higher player activity during this period, thus offering high calculation efficiency. The advanced formula is suitable for longer time spans and offers higher accuracy. Activity level is a coefficient calculated by the system based on recorded player behavior data, used to measure a player's value or representativeness. Activity-related data can be collected through player behavior logs, determined based on factors such as login frequency, game duration, number of matches, task completion rate, and frequency of social interaction.

[0051] For example, the quantification value of the Material Ownership Rate (MOR) metric is determined based on the ratio of the average number of training materials held by each player to the total number of training materials required for card development, or based on the training material ownership rate at different development stages and the weights of different development stages. That is, there is a basic formula for calculating the quantification value of the Material Ownership Rate metric: MOR = Number of training materials held by the player / Total number of materials required for card development, and a more advanced formula for calculating the quantification value of the Material Ownership Rate metric: MOR = Σ(Training material ownership rate required for stage i × Weight of stage i). Here, the number of training materials held by a player represents the overall training material holdings of all players; it can be understood as the training material holdings of a particular card by active players over a period of time. The basic formula calculates the average value by calculating the holdings of active players. Stage i refers to different development levels, which include various parallel attributes such as card level, skill level, enhancement level, and talent level. Since the materials required for training at different levels may vary, after calculating the card training material holding rate for each stage according to the basic formula, the overall card training material holding rate can be quantified based on the weight ratio of each stage.

[0052] For example, the Card Usage Frequency (CUF) metric is quantified based on the difference between the number of times a card is used within a specific time window and the total number of times all cards are used within that time window. The Card Usage Frequency metric can be used to evaluate how frequently cards are used in various game modes.

[0053] For example, the Card Win Contribution (CWC) metric is quantified based on the difference between the win rate of a lineup that includes a card and the average win rate of a similar lineup that does not include the same card. It can objectively reflect the net increase in win rate caused by that card.

[0054] For example, the Community Heat Index (CHI) is determined by the number of times a card is mentioned on social media and official forums, as well as the amount of content created about the card. Card heat can be calculated by obtaining information such as posts from forum players, identifying and processing keywords, and also by obtaining data from in-game player chat. After normalizing the number of mentions on social media and official forums, and the amount of content created about the card, the quantitative value of the card heat index can be determined by CHI = 0.4 × number of social media mentions + 0.3 × number of official forum mentions + 0.3 × number of card content creations. The weights of the number of mentions on social media and official forums, and the amount of card content created can be selected and adjusted according to the card's release scenario, and the sum of the weights should be 1.

[0055] In some embodiments, before performing step 013, the method further includes: Please see Figure 2 Step 0130: Normalize the quantitative values ​​of each preset attractiveness evaluation index.

[0056] Specifically, before calculating the card attractiveness index value, it is necessary to normalize the quantitative values ​​of the general attractiveness index in the preset attractiveness evaluation index corresponding to each card. Normalization methods include min-max normalization and logarithmic transformation normalization. Min-max normalization maps the data linearly to a closed interval of 0 to 1 using the formula: Normalized value = (Original value - Minimum original value) / (Maximum original value - Minimum original value). This is suitable for data with clear and stable boundaries that are unlikely to have extreme outliers. Weighted average normalization maps the data linearly to a closed interval of 0 to 1 by multiplying the quotient of the difference between the original value and the mean by the standard deviation, and then multiplying by the weight corresponding to the original value. This is suitable for data that needs to highlight key features. Logarithmic transformation normalization takes the natural logarithm or base-10 logarithm of all original data and then performs min-max normalization. This is suitable for handling data with extreme outliers and data spanning multiple orders of magnitude.

[0057] For example, in the quantitative calculation of card design metrics, the scores can first be mapped to a closed interval of 0 to 1 using min-max normalization, and then a weighted average can be calculated based on the scores. If the quantitative results of card lineup positioning and card holding rate metrics meet the requirements, a weighted average or ratio can be directly calculated. In the quantitative calculation of card development level metrics, if using a basic formula, a weighted average normalization should be performed on the card development levels before calculating the quantitative value of the card development level metric; if using an advanced formula, a weighted average normalization should be performed on the activity-related factors of individual players before calculating the quantitative value of the card development level metric. In the quantitative calculation of card training material holding rate, if using a basic formula, directly calculating the material holding rate meets the result interval requirements; if using an advanced formula, a weighted average normalization should be performed on the distribution of materials of different rarities before calculating the quantitative value of the card development level metric. In the quantitative calculation of card usage frequency and card popularity metrics, the frequency can be logarithmically transformed and then normalized. In the quantitative calculation of card win rate contribution metrics, the win rate should be normalized using min-max normalization.

[0058] In some embodiments, step 013 includes: Please see Figure 2 Step 0131: Based on at least one of the following quantitative values ​​for each card: card design scoring index, card lineup positioning index, card holding rate index, card development level index, card training material holding rate index, card usage frequency index, card win rate contribution index, and card popularity index, determine the attractiveness index value for each card.

[0059] Specifically, the card's attractiveness index value should be based on one or more of the general attractiveness indices in a preset attractiveness evaluation index. First, determine the quantitative values ​​of the general attractiveness indices corresponding to the preset attractiveness evaluation index, and then use these quantitative values ​​to determine the card's attractiveness index value, so as to comprehensively evaluate the card's attractiveness.

[0060] In some embodiments, the attractiveness index value is determined based on the following formula: CAS = ω1×CDS + ω2×CPV + ω3×(1-COR) + ω4×(1-CDL) + ω5×(1-MOR)+ ω6×CUF + ω7×CWC + ω8×CHI (1) Wherein, ω1 to ω8 are weight coefficients with a sum of 1, CAS is the attractiveness index value, CDS is the quantitative value of the card design rating index, CPV is the quantitative value of the card lineup positioning index, COR is the quantitative value of the card holding rate index, CDL is the quantitative value of the card development level index, MOR is the quantitative value of the card training material holding rate index, CUF is the quantitative value of the card usage frequency index, CWC is the quantitative value of the card win rate contribution index, and CHI is the quantitative value of the card popularity index.

[0061] It's understandable that the card's attractiveness index increases positively with increases in card design rating, card lineup positioning, card usage frequency, card win rate contribution, and card popularity. Therefore, in the formula, the normalized values ​​of these positively correlated indicators can be directly multiplied by their weighting coefficients. Conversely, the card's attractiveness index increases negatively with decreases in card ownership rate, card development level, and card training material ownership rate. Therefore, in the formula, the difference between 1 and the normalized values ​​of these negatively correlated indicators needs to be multiplied by their weighting coefficients. These weighting coefficients are values ​​between 0 and 1, and can be either 0 or 1, with a total weighting coefficient of 1.

[0062] Specifically, the card's attractiveness index value can be calculated by weighting the normalized quantified values ​​of various general attractiveness indicators for the card to be evaluated. Higher weights indicate a greater influence of that general attractiveness indicator; higher quantified values ​​of each general attractiveness indicator result in a higher card attractiveness index value; and a higher card attractiveness index value indicates stronger card attractiveness.

[0063] Step 014: Based on the attractiveness index values ​​of various virtual items, recommend target virtual items or combinations of target virtual items.

[0064] The following explanation and illustration will be based on card games.

[0065] For example, in card games, based on the attractiveness index value of virtual items obtained in step 013, when there is a need to evaluate the design of new cards, the potential attractiveness of the cards can be assessed more scientifically during the card design stage; when there is a need to select cards for game events, the most attractive cards can be selected for the game events; when there is a need to optimize the card pool combination, the optimal card distribution combination can be designed; when there is a need to adjust the card balance, cards that need to be strengthened or weakened can be identified; and when there is a need for personalized recommendations, the most attractive cards can be recommended to different players.

[0066] This application provides an information processing method for games. Taking cards as an example, the method includes: collecting relevant information about various cards in the card game to provide data support for subsequent card attractiveness assessment. The relevant card information may include user behavior data, card attribute data, and game economic data. Based on the relevant card information, the method calculates the quantitative values ​​of multiple preset attractiveness assessment indicators related to card attractiveness for each card. These preset attractiveness assessment indicators may include multiple general attractiveness indicators, such as card design rating indicators, card lineup positioning indicators, card holding rate indicators, card development level indicators, card training material holding rate indicators, card usage frequency indicators, card win rate contribution indicators, and card popularity indicators. Based on the quantitative values ​​of these preset attractiveness assessment indicators for each card, the attractiveness indicator values ​​for each card are determined. This application embodiment, by collecting relevant card information and calculating multiple preset attractiveness assessment indicators related to cards, determines the attractiveness indicator values ​​for various cards, enabling more efficient and objective evaluation of card attractiveness, recommendation of cards or card combinations, reduction of the designer's burden of card attractiveness assessment, and improvement of card deployment efficiency.

[0067] Please see Figure 3 In some embodiments, the information processing method in the game further includes: Step 015: Adjust the various weighting coefficients to suit the target player group and the current game stage.

[0068] The player base can be categorized into beginner players, intermediate players, advanced players, and paying players.

[0069] The game stages can include the beginner stage, the development stage, the bottleneck stage, and the maintenance stage. Players have different levels of familiarity with the game rules and different amounts of resources accumulated in the game world at different stages.

[0070] Specifically, in the formula for determining the attractiveness index value of a card, the weight of each general attractiveness index represents the degree of influence of that index. By adjusting the weight coefficients, the suitability of the card's attractiveness index for specific scenarios can be adjusted according to different target player groups and the current stage of the game.

[0071] For example, when players are in the early stages of the game, their levels are low, they have a limited understanding of the game rules, their card pool is relatively small, and their resources (gold and materials) are extremely scarce. Their core objective is to survive and gain positive feedback. For players at this stage, cards with distinctive characters, easy-to-use features, and the ability to immediately enhance the gaming experience are more attractive. Therefore, the weighting coefficients of card design metrics, card lineup positioning metrics, and card usage frequency metrics can be appropriately increased.

[0072] For example, a player in the early game development phase, whose level is gradually increasing, has become familiar with the basic rules, has developed their first core lineup, and begins to purposefully accumulate resources. The core objective is to strengthen the existing lineup and improve overall strength. For players at this stage, cards that can effectively enhance their existing lineup and have a high cost-effectiveness in development are more attractive. Therefore, the weighting coefficients of the inverse value (1-MOR) of card lineup positioning indicators, card win rate contribution indicators, card development level indicators, and card training material holding rate indicators can be appropriately increased.

[0073] For example, a player may be in a bottleneck phase of the game, having already developed their main lineup and reached a top-tier level of strength, finding it difficult to improve further. They may be highly active in the community and have accumulated some resources but are unsure how to invest them. The core objective is to break out of this stalemate and find new avenues for improvement. For players at this stage, cards that can unlock new lineups, possess strategic value, are extremely rare, or inspire new goals are more attractive. Appropriately increasing the inverse correlation coefficient (1-COR) of card ownership and the card popularity index could be beneficial.

[0074] For example, a player in the "maintenance phase" has become a seasoned player with a deep card collection and abundant resources. The game's objective shifts from becoming stronger to maintaining status, pursuing perfection, and collecting. The core goals are maintaining competitive advantage, completing the ultimate collection, and gaining a sense of identity. For players at this stage, cards with top-notch design, collectible value, and social bragging rights are more attractive. Appropriately increasing card ownership and popularity metrics is advisable.

[0075] Please see Figure 4 In some embodiments, the preset attractiveness evaluation index also includes a dedicated attractiveness evaluation index adapted to different player groups, and step 013 further includes: Step 0132: Based on the quantitative values ​​of the general attractiveness index corresponding to each virtual item and the specific attractiveness index corresponding to the target player group, determine the attractiveness index value of each virtual item.

[0076] Specifically, the preset attractiveness assessment indicators include general attractiveness indicators that can provide an attractiveness assessment reference in various scenarios, as well as more specific attractiveness assessment indicators for different player groups. When dealing with the specific virtual item distribution issues for different player groups, the general attractiveness indicators and the specific attractiveness indicators can be combined to calculate the attractiveness indicator values ​​of various virtual items that are more relevant to specific player groups, thereby guiding virtual item distribution decisions.

[0077] In some embodiments, different player groups include novice players, intermediate players, advanced players, and paying players; The specific attractiveness metrics for novice players include ease of use and beginner friendliness. The quantification value of the ease of use metric is based on the third preset value of intuitive effect and the fourth preset value of strategy complexity. The quantification value of the beginner friendliness metric is based on the fifth preset value of difficulty of understanding, the sixth preset value of usage threshold and the seventh preset value of beginner utility. The specific attraction metrics for intermediate players include team composition and development efficiency metrics. The quantitative value of the team composition metric is based on the eighth preset value of generality and the ninth preset value of synergy. The quantitative value of the development efficiency metric is based on the tenth preset value of initial skills, the eleventh preset value of the growth curve, and the twelfth preset value of resource requirements. The specific attractiveness metrics for advanced player groups include rarity and competitive value metrics. The rarity metric is quantified based on the thirteenth preset value of acquisition difficulty, the fourteenth preset value of planned deployment, and the fifteenth preset value of time restriction. The competitive value metric is quantified based on usage frequency and win rate. The specific attraction metrics for the paying player group include uniqueness and collectible value metrics. The quantification value of the uniqueness metric is based on the sixteenth preset value of visual uniqueness and the seventeenth preset value of thematic uniqueness. The quantification value of the collectible value metric is based on the eighteenth preset value of limited availability and the nineteenth preset value of IP linkage.

[0078] The following explanation and illustration will be based on card games.

[0079] For example, for novice players, the weight of usability and beginner-friendliness metrics should be increased on top of general attractiveness metrics.

[0080] The Card Usability Index (CUI) can be calculated as: CUI = 0.6 × Third preset value of effect intuitiveness + 0.4 × Fourth preset value of strategy complexity. The weights of the third and fourth preset values ​​of effect intuitiveness and strategy complexity can be selected and adjusted according to the card's deployment scenario, and the sum of the weights should be 1. Both the third and fourth preset values ​​of effect intuitiveness and strategy complexity are derived during the design phase through a scoring system (out of 10), which is normalized using min-max normalization. A score of 10 for effect intuitiveness indicates the card effect is most intuitive; 1-3 indicates the card effect is abstract and requires a deeper understanding of the game mechanics; 4-6 indicates the card effect is relatively clear but has some hidden details; and 7-10 indicates the card effect is clear and immediately understandable. A strategy complexity score of 10 indicates the simplest card strategy. A score of 1-3 indicates that card use requires complex strategic planning and precise timing. A score of 4-6 indicates that card use requires some strategic consideration. A score of 7-10 indicates that card use strategy is simple and easy to decide.

[0081] The Card Beginner Friendliness (CBF) metric can be calculated as follows: CBF = 0.4 × Fifth preset value for difficulty of understanding + 0.3 × Sixth preset value for usability + 0.3 × Seventh preset value for beginner utility. The weights of these three preset values ​​can be selected and adjusted based on the card's deployment scenario, and their sum should be 1. These preset values ​​are derived during the design phase through a scoring system (out of 10), and the scores are normalized using min-max normalization. A score of 10 indicates the card design is the easiest to understand; 1-3 indicates that in-depth study is required to understand the card's effects and usage timing; 4-6 indicates that careful reading of the instructions and consideration of the usage scenario are necessary; and 7-10 indicates that the card is simple and clear, and its effects and usage methods are easy to understand. When the usage threshold is 10, it means the card has the lowest barrier to entry. 1-3 indicates a deep understanding of the game mechanics and interactions with other cards is required for effective use. 4-6 indicates some game experience is needed for proper use. 7-10 indicates even beginners can easily determine when and how to use it. When the beginner utility is 10, it means the card is most useful for beginners. 1-3 indicates the card is primarily aimed at advanced players, and beginners may find it difficult to realize its value. 4-6 indicates the card has some use for all types of players. 7-10 indicates the card is particularly suitable for beginners and can significantly improve their gaming experience.

[0082] For novice players, the calculation of the attractiveness index of their cards should be based on formula (1) with the addition of two terms: ω8×CUI and ω9×CBF.

[0083] For example, for the attraction rating of mid-range players, the weight of team composition and development efficiency should be increased on the basis of general attraction indicators.

[0084] The Card Composition Versatility (CCV) metric can be calculated as: CCV = 0.6 × Eighth preset value of versatility + 0.4 × Ninth preset value of synergy. The weights of the eighth and ninth preset values ​​of versatility can be selected and adjusted based on the card's deployment scenario, and the sum of the weights should be 1. Both the eighth and ninth preset values ​​of versatility are derived during the design phase through a scoring system (out of 10), which is then normalized using min-max normalization. A versatility score of 10 indicates the card is the most versatile; 1-3 indicates the card is only suitable for specific team compositions; 4-6 indicates the card has some use in various team compositions; and 7-10 indicates the card is applicable to almost all team compositions. Although card versatility has already been included in the calculation of the CCV metric for card team composition positioning, this calculation focuses on considering the universality of cards sought by mid-level players. A score of 10 indicates the strongest card synergy; 1-3 indicates less interaction between the card and other cards; 4-6 indicates good synergy with some cards; and 7-10 indicates strong synergy with most cards.

[0085] The Card Development Efficiency (CDE) quantification value can be calculated as follows: CDE = 0.4 × the tenth preset value of initial skill + 0.3 × the eleventh preset value of growth curve + 0.3 × the twelfth preset value of resource requirement. The weights of the tenth preset value of initial skill, the eleventh preset value of growth curve, and the twelfth preset value of resource requirement can be selected and adjusted according to the card deployment scenario, and the sum of the weights should be 1. The tenth preset value of initial skill, the eleventh preset value of growth curve, and the twelfth preset value of resource requirement are all obtained during the design phase through scoring (out of 10), and the scores are normalized using min-max normalization. An initial efficiency of 10 points indicates the card has the highest initial efficiency; 1-3 points indicate the card requires significant training to be effective; 4-6 points indicate the card has some effect in its initial state; and 7-10 points indicate the card is very powerful in its initial state. Cards grow fastest when the growth curve is 10 points. 1-3 points indicate slow growth requiring long-term investment; 4-6 points indicate moderate growth; and 7-10 points indicate rapid growth with significant effect enhancement. Similarly, cards have the lowest resource requirements when the resource requirement is 10 points. 1-3 points indicate a large need for rare resources; 4-6 points indicate moderate resource requirements; and 7-10 points indicate low resource requirements and ease of development.

[0086] For intermediate players, the calculation of the attractiveness index of their cards should be based on formula (1) with the addition of two terms: ω8×CCV and ω9×CDE.

[0087] For example, when scoring the appeal of high-end players, the weights of rarity and competitive value should be increased in addition to general appeal metrics.

[0088] The Card Rarity Index (CRI) can be quantified using the following formula: CRI = 0.4 × 13th preset value of acquisition difficulty + 0.3 × 14th preset value of planned distribution volume + 0.3 × 15th preset value of time restriction. The weights of the 13th, 14th, and 15th preset values ​​of difficulty, planned distribution volume, and time restriction can be selected and adjusted according to the card distribution scenario, and the sum of the weights should be 1. The 13th, 14th, and 15th preset values ​​of acquisition difficulty, planned distribution volume, and time restriction are all derived during the design phase through a scoring system (out of 10), and the scores are normalized using min-max normalization. A difficulty score of 10 indicates the card is the most difficult to obtain; 1-3 indicates the card is common and easy to obtain; 4-6 indicates the card requires a certain investment to obtain; and 7-10 indicates the card is extremely difficult to obtain and may require special methods. A planned release score of 10 indicates the lowest card supply; 1-3 indicates ample planned release; 4-6 indicates limited release, but not scarce; and 7-10 indicates extremely limited release. A time-limited release score of 10 indicates the strictest time restriction; 1-3 indicates the card is permanently available in the pool and readily obtainable; 4-6 indicates the card appears periodically; and 7-10 indicates the card is available only during limited-time events or as a one-time sale.

[0089] The Card Race Value (CRV) metric will not be further quantified. Compared to the weighting of metrics in the general calculation formula, the weighting of the card usage frequency metric and the card win rate contribution metric should be increased for advanced players.

[0090] For advanced players, the calculation of the attractiveness index value of their cards should be based on formula (1) with the addition of ω8×CRI and the increase of the weights of CUF and CWC.

[0091] For example, when scoring the attractiveness of paying players, the weights of uniqueness and collectible value should be increased in addition to general attractiveness metrics. The Card Uniqueness (CUQ) metric can be calculated as: CUQ = 0.4 × the sixteenth preset value of visual uniqueness + 0.6 × the seventeenth preset value of thematic uniqueness. The weights of the sixteenth and seventeenth preset values ​​of visual uniqueness can be selected and adjusted according to the card's deployment scenario, and the sum of the weights should be 1. Both the sixteenth and seventeenth preset values ​​of visual uniqueness are derived during the design phase through a scoring system (out of 10), which is then normalized using min-max normalization. A visual uniqueness score of 10 indicates the card is the most unique; 1-3 indicates the card has an ordinary appearance with no special visual effects; 4-6 indicates the card has some distinctive features and good visual effects; and 7-10 indicates the card has stunning visual effects and is highly recognizable. A score of 10 for theme uniqueness indicates that the card is the most unique; 1-3 indicates that the card theme is common and lacks originality; 4-6 indicates that the card theme is interesting but not unique; and 7-10 indicates that the card theme is novel and brings new elements to the game.

[0092] The Collection Value Index (CVI) can be calculated as follows: CVI = 0.5 × the 18th preset value of the limited availability + 0.5 × the 19th preset value of the IP collaboration. The weights of the 18th and 19th preset values ​​of the limited availability and IP collaboration can be selected and adjusted according to the card's release scenario, and the sum of the weights should be 1. Both the 18th and 19th preset values ​​of the limited availability and IP collaboration are derived during the design phase through a scoring system (out of 10), which is then normalized using min-max normalization. A limited availability score of 10 indicates the card is extremely limited; 1-3 indicates the card is not limited and can be obtained at any time; 4-6 indicates the card is temporarily limited and may be released again in the future; and 7-10 indicates the card is a limited edition and will not be released again. When the IP collaboration score is 10, it means that the card collaboration has the highest artistic value. 1-3 points means that the card IP recognition is average. 4-6 points means that the card IP recognition is excellent. 7-10 points means that the card IP recognition is extremely high and can be called a work of art.

[0093] For paying players, the calculation of the attractiveness index value of their cards should be based on formula (1) with the addition of two items: ω8×CUQ and ω9×CVI.

[0094] Please see Figure 5 In some embodiments, the information processing method for card games further includes: Step 016: Based on at least one of the following: the target player group, the expected holding rate of various cards, and the attractiveness index value of various cards, output decision suggestions.

[0095] For example, in a large-scale card game, a certain spell card set is about to be capped. Initially, these cards generally exhibit high popularity in terms of art design and community buzz, but their actual usage frequency and win rate remain consistently low. The quantified values ​​for general appeal metrics might be: CDS = 9.1 / 10 (highly praised art and skill creativity); CHI = 85 / 100 (maximum community anticipation, abundant fan-created content); CUF = 0.8% (extremely low usage rate across all skill levels); CWC = 0.05 (low win rate for decks using this card). This indicates a design flaw where the card's skill mechanics are out of sync with the combat environment. Based on the specific appeal metrics, card optimization suggestions can be made: shorten the trigger delay of core skills to improve reaction speed; fine-tune skill values ​​to increase attack power and enhance its battlefield impact, etc.

[0096] For example, during game seasons or anniversary events, significant version updates and adjustments to card distribution are required. Based on card information, it can be predicted that the appeal of an older card A with a certain ability may increase during this period because this card effectively counters popular strategies in the new version. This card, due to its high cost-effectiveness and its effectiveness against environmental counters, is more attractive to intermediate and advanced players. However, given that the current card ownership rate is relatively low, below the expected ownership rate after the game version update, the timing of card A's release can be leveraged. It can be released simultaneously with the new version launch, targeting all players with a slight increase in the draw probability in the regular card pool. For intermediate and advanced players, a high-priced bundle containing card A and exclusive upgrade materials can be offered in the limited-time privilege shop to meet their urgent need for rapid character development. Furthermore, if card A has a stronger synergy with another card B with similar appeal metrics, these two cards can be bundled together to form a "Sealed Set" for joint promotion. This can effectively increase the influence of card B and enhance the overall value of the event.

[0097] To better illustrate the information processing method in the game provided in the embodiments of this application, please refer to... Figure 6 This application provides a card distribution system.

[0098] This system achieves a highly automated process for evaluating and deploying card attractiveness through the collaborative work of a data acquisition module, an indicator evaluation module, an attractiveness calculation module, and a decision support module.

[0099] The data acquisition module includes a user behavior data acquisition unit, a card attribute data acquisition unit, and a game economy data acquisition unit. The user behavior data acquisition unit automatically collects real-time and historical logs of player behavior in the game, such as card acquisition through card draws and quests, specific card usage in Player vs. Player (PVP) and Player vs. Player Environment (PVE) modes, and player actions related to card upgrades, advancements, and skill upgrades. The card attribute data acquisition unit extracts static data from the game database, including: basic card values ​​such as attack, health, and speed; card skill descriptions, rarity, class, faction; and the overall distribution of cards in the player's card pool. The game economy data acquisition unit collects economic data related to player-monitored cards, including virtual or real currency spent on card acquisition or development, and the quantity of various materials consumed during the development process. After data collection is completed, the system aggregates these three types of raw data into a unified multidimensional data pool and stores it in the memory of the electronic device, laying the foundation for subsequent analysis.

[0100] The indicator evaluation module is used to determine general attractiveness indicators related to cards. These indicator values ​​are based on data stored in the data acquisition module. Specifically, the card design indicator evaluates the card's art style, skill creativity, and balance. The card lineup positioning indicator evaluates the card's coreness, versatility, and ease of substitution within mainstream lineups. The card ownership rate indicator assesses the proportion of the target player group that owns the card. The card progression indicator assesses the average progression progress of the card among players. The card training material ownership rate indicator analyzes the level of training materials players need for the card. The card usage frequency indicator tracks the card's appearance rate within a specific time window. The card win rate contribution indicator calculates the card's actual contribution to winning. The card popularity indicator analyzes communities, forums, and content platforms to assess the card's discussion volume. The quantitative values ​​of these general attractiveness indicators should be normalized based on data distribution and other factors. After the indicator evaluation, the card-related data is converted into indicator data that more intuitively reflects the card's performance in the game.

[0101] The attraction calculation module receives the quantified value of the general attraction index from the index evaluation module. First, in the index configuration unit, according to the game development team's instruction to update the index weights, a reference general attraction index is selected, and the weight coefficients of each general attraction index are adjusted. Then, in the attraction algorithm unit, the quantified value of the general attraction index and the weight coefficients are substituted into the card attraction index calculation formula as shown in formula (1) to obtain the preliminary card attraction index value. Since the card attraction is mainly reflected in the player's game behavior and experience, it needs to be evaluated more accurately according to different player groups. Then, in the player stratification evaluation unit, the weight coefficients of the general attraction index should be adjusted or special attraction indexes should be added according to different player groups. Among them, different player groups can be beginner, intermediate, and advanced players divided according to game level, or experience players, paying players, etc. determined by behavioral data such as payment amount, activity level, and competitive ranking. After the attraction calculation is completed, the attraction index values ​​of various cards for different player groups can be output.

[0102] The decision support module uses the card attractiveness index values ​​calculated by the attractiveness calculation module to recommend deployment strategies, predict attractiveness trends, and generate card optimization suggestions. The card optimization suggestion generation unit provides card design optimization suggestions based on the card's attractiveness index values. For example, it suggests increasing deployment efforts for cards with high attractiveness index values ​​and monitoring general and specific attractiveness indicators in greater detail to update and optimize card designs in a timely manner. For cards with low attractiveness index values, it suggests significantly modifying the card design to improve its attractiveness. The deployment strategy recommendation unit recommends card deployment timing, channels, and prices based on the card's attractiveness index values. For example, during anniversary celebrations and other events, the initial card's attractiveness index value should be higher, and the higher the attractiveness index value, the higher the price should be. The trend prediction unit predicts changes in card attractiveness trends. For example, it monitors changes in the attractiveness index value of initial cards over a period of time; if the attractiveness shows a downward trend, a design update strategy can be implemented. The final decision support module provides feedback to the game development team on the deployment strategy, attractiveness prediction, and card optimization suggestions for specific scenarios. The game development team can update the weight configuration based on the feedback results and continue to control the system to enter the data collection module to continuously monitor card-related information.

[0103] This application provides a method for determining card attractiveness by collecting card-related information, calculating the quantitative values ​​of preset general and specific attractiveness indicators to determine the card's attractiveness index value, and outputting decision-making suggestions based on the evaluation process. This method can improve the scientific rigor and relevance of card design, reduce reliance on designers' experience, optimize card distribution strategies, increase game revenue, enhance players' willingness to acquire cards, improve user stickiness and satisfaction, provide data support for game balance adjustments, optimize the game experience, reduce the learning cost of card design and distribution, and improve team efficiency.

[0104] All of the above technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0105] To facilitate better implementation of the information processing method in games according to the embodiments of this application, the embodiments of this application also provide an information processing apparatus for games. Please refer to... Figure 7 , Figure 7 This is a schematic diagram of the structure of an information processing device in a game provided in an embodiment of this application. The information processing device 200 in the game may include: The data acquisition module 201 is used to collect relevant information about various virtual items; The calculation module 202 is used to calculate the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to various virtual items based on the relevant information. The determination module 203 is used to determine the attractiveness index value of each virtual item based on the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to each virtual item. Recommendation module 204 is used to recommend target virtual items or combinations of target virtual items based on the attractiveness index values ​​of various virtual items.

[0106] It should be noted that the functions of each module in the information processing device 200 in the game in this application embodiment can be referred to the specific implementation of any embodiment in the above method embodiments, and will not be repeated here.

[0107] Each unit in the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each unit can be embedded in or independent of the processor in the electronic device in hardware form, or stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to each unit.

[0108] For example, the information processing device 200 in the game can be integrated into a terminal or server that has storage and a processor and thus computing power, or the information processing device 200 in the game can be that terminal or server.

[0109] In some embodiments, this application also provides an electronic device including a memory and a processor, wherein the memory stores one or more computer instructions, and the processor executes the stored one or more computer instructions to implement the steps in the above method embodiments.

[0110] Figure 8 A schematic structural diagram of the electronic device provided in the embodiments of this application, such as... Figure 8 As shown, the electronic device 300 may include: a communication interface 301, a memory 302, a processor 303, and a communication bus 304. The communication interface 301, memory 302, and processor 303 communicate with each other via the communication bus 304. The communication interface 301 is used for data communication between the device 300 and external devices. The memory 302 can be used to store software programs and modules, and the processor 303 runs the software programs and modules stored in the memory 302, such as the software programs for the corresponding operations in the aforementioned method embodiments.

[0111] In some embodiments, the processor 303 may invoke software programs and modules stored in the memory 302 to perform the following operations: Collect information about various cards in the card game; Based on relevant information, calculate the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to each type of card; Based on the quantitative values ​​of multiple preset attractiveness evaluation indicators corresponding to each type of card, the attractiveness indicator values ​​of each type of card are determined respectively.

[0112] In some embodiments, the electronic device 300 may be integrated into a terminal or server that has storage and a processor and thus computing power, or the electronic device 300 may be the terminal or server.

[0113] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to an electronic device, and the computer program causes the electronic device to execute the corresponding processes in the methods described above in the embodiments of this application; for brevity, these will not be elaborated further here.

[0114] This application also provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the corresponding processes in the methods described above in the embodiments of this application. For brevity, these details will not be elaborated further here.

[0115] This application also provides a computer program that includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the corresponding processes in the methods described above in the embodiments of this application. For the sake of brevity, these details will not be elaborated further here.

[0116] It should be understood that the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0117] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0121] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, the functional units in the embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0124] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer or a server) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0125] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An information processing method in a game, characterized by, The method comprises: collecting relevant information of various virtual items; based on the relevant information, calculating the quantified values of a plurality of preset attraction evaluation indexes corresponding to each of the various virtual items; based on the quantified values of the plurality of preset attraction evaluation indexes corresponding to each of the various virtual items, respectively determining attraction index values of the various virtual items; based on the attraction index values of the various virtual items, recommending a target virtual item or a target combination of virtual items.

2. The information processing method in a game according to Claim 1, characterized by, The virtual items include cards in card games, equipment in shooting games, and skins in role-playing games, etc., the relevant information includes user behavior data, virtual item attribute data, and game economy data, the user behavior data includes data of various behaviors performed by players on virtual items, the virtual item attribute data includes data related to the attributes of virtual items, and the game economy data includes data related to the consumption of virtual items, and the calculation of the quantified values of the plurality of preset attraction evaluation indexes corresponding to each of the various virtual items based on the relevant information comprises: based on the user behavior data, the virtual item attribute data, and the game economy data corresponding to each of the various virtual items, respectively calculating the quantified values of the plurality of preset attraction evaluation indexes corresponding to each of the various virtual items.

3. The information processing method in a game according to Claim 1 or 2, characterized by, The preset attraction evaluation indexes include a plurality of general attraction indexes, and the virtual items include cards, and the general attraction indexes of the cards include at least one of a card design score index, a card lineup positioning index, a card possession rate index, a card nurturing degree index, a card training material possession rate index, a card use frequency index, a card win rate contribution index, and a card popularity index; The card design score index is used to evaluate at least one of the artistic performance, skill creativity, and balance of the card design; The card lineup positioning index is used to evaluate at least one of the core degree of the card in the lineup, the universality of the card, and the difficulty of replacing the card; The card possession rate index is used to evaluate the possession rate of the card; The card nurturing degree index is used to evaluate the difficulty of nurturing the card; The card training material possession rate index is used to evaluate the possession rate of the material for training the card; The card use frequency index is used to evaluate the use frequency of the card; The card win rate contribution index is used to evaluate the win rate when the card is used; The card popularity index is used to evaluate the popularity of the card among players.

4. The information processing method in a game according to Claim 3, characterized by, The quantified values of the general attraction indexes of the virtual items can be determined from the information matching the general attraction indexes in the relevant information of the corresponding virtual items.

5. The information processing method in a game according to Claim 4, characterized by, The determination of the attraction index values of the various virtual items based on the quantified values of the plurality of preset attraction evaluation indexes corresponding to each of the various virtual items comprises: determining the attraction index values of the various cards based on at least one of the quantified values of the card design score index, the card lineup positioning index, the card possession rate index, the card nurturing degree index, the card training material possession rate index, the card use frequency index, the card win rate contribution index, and the card popularity index corresponding to each of the various cards.

6. The information processing method in a game according to Claim 5, characterized by, The attraction index values are determined based on the following formula: CAS = ω1×CDS + ω2×CPV + ω3×(1-COR) + ω4×(1-CDL) + ω5×(1-MOR) +ω6×CUF + ω7×CWC + ω8×CHI; Wherein, ω1 to ω8 are weight coefficients and the sum is 1, the CAS is the attraction index value, the CDS is the quantitative value of the card design score index, the CPV is the quantitative value of the card lineup positioning index, the COR is the quantitative value of the card holding rate index, the CDL is the quantitative value of the card development degree index, the MOR is the quantitative value of the card training material holding rate index, the CUF is the quantitative value of the card use frequency index, the CWC is the quantitative value of the card win rate contribution index, and the CHI is the quantitative value of the card heat index.

7. The information processing method in a game according to Claim 6, characterized by, Also includes: Adjusting each of the weight coefficients to adapt to the target player group and the current game stage.

8. The information processing method in a game according to Claim 3, characterized by, The preset attraction evaluation index also includes a special attraction evaluation index adapted to different player groups, and the quantitative values of various virtual items corresponding to a plurality of preset attraction evaluation indexes respectively determine the attraction index values of various virtual items, including: Based on the quantitative values of the general attraction index and the special attraction index corresponding to the target player group, the attraction index values of various virtual items are determined respectively.

9. The information processing method in a game according to Claim 8, characterized by, The different player groups include a primary player group, an intermediate player group, an advanced player group, and a paying player group; The special attraction index corresponding to the primary player group includes an ease of use and a beginner friendliness index, the quantitative value of the ease of use index is based on a third preset value of effect intuitiveness and a fourth preset value of strategy complexity, and the quantitative value of the beginner friendliness index is based on a fifth preset value of understanding difficulty, a sixth preset value of use threshold, and a seventh preset value of beginner utility; The special attraction index corresponding to the intermediate player group includes a lineup combination and a development efficiency index, the quantitative value of the lineup combination index is based on an eighth preset value of universality and a ninth preset value of synergy effect, and the quantitative value of the development efficiency index is based on a tenth preset value of initial skill, an eleventh preset value of growth curve, and a twelfth preset value of resource demand; The special attraction index corresponding to the advanced player group includes a rarity and a competitive value index, the quantitative value of the rarity index is based on a thirteenth preset value of acquisition difficulty, a fourteenth preset value of planned launch amount, and a fifteenth preset value of time limit, and the quantitative value of the competitive value index is based on use frequency and win rate; The special attraction index corresponding to the paying player group includes a uniqueness and a collection value index, the quantitative value of the uniqueness index is based on a sixteenth preset value of visual uniqueness and a seventeenth preset value of theme uniqueness, and the quantitative value of the collection value index is based on an eighteenth preset value of limitation degree and a nineteenth preset value of IP linkage.

10. An information processing apparatus in a game, comprising: The device includes: An acquisition module for acquiring relevant information of various different virtual items; The computing module is configured to calculate quantified values of the multiple preset attraction evaluation indexes corresponding to each of the virtual items based on the correlation information. The determining module is configured to determine attraction index values of the virtual items respectively based on the quantified values of the multiple preset attraction evaluation indexes corresponding to each of the virtual items. The recommending module is configured to recommend a target virtual item or a target virtual item combination based on the attraction index values of the virtual items.

11. An electronic device, comprising: comprising: a memory and a processor; the memory is configured to store one or more computer instructions; the processor is configured to execute the one or more computer instructions to implement the method according to any one of claims 1-9.

12. A computer-readable storage medium having stored thereon one or more computer instructions, wherein, The instructions are executed by the processor to implement the method according to any one of claims 1-9.

13. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the method according to any one of claims 1-9.

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