WO2017167115A1 - 用户评价方法、装置及设备 - Google Patents

用户评价方法、装置及设备 Download PDF

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Publication number
WO2017167115A1
WO2017167115A1 PCT/CN2017/077901 CN2017077901W WO2017167115A1 WO 2017167115 A1 WO2017167115 A1 WO 2017167115A1 CN 2017077901 W CN2017077901 W CN 2017077901W WO 2017167115 A1 WO2017167115 A1 WO 2017167115A1
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game
user
information
evaluation
consumption information
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English (en)
French (fr)
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杨志军
戢洋
吴保华
甘云锋
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations

Definitions

  • the present invention relates to the field of computers, and in particular, to a user evaluation method, apparatus and device.
  • the modern era is an Internet age. In every aspect and every aspect of people's lives, the Internet is everywhere. The Internet has brought about changes in people's lives, and at the same time, it has spawned many emerging industries.
  • the online game industry is one of the products of the Internet age. Online games have become a major form of entertainment in today's society, especially for office workers. In order to find potential game players (hereinafter also referred to as game users) and promote online games, online game developers need to evaluate the user's game value.
  • the user's game value is the value of the user's promotion of online games. The higher the value of the user's game, the higher the value of promoting online games to users. The higher the value of the game, the more likely it is that the user will accept the promoted online game. Therefore, the user's game value evaluation score has great commercial value for online game developers.
  • the value of the game of the user is evaluated by the game user's consumption and behavior within a certain game.
  • This way of evaluating the value of the user's game based solely on the user's consumption and behavior in the single product game is obviously not objective. For example, a user consumes a small amount within a single item game, but the user consumes a lot in another similar game. Obviously, this user is a user with high game value, but the online game developer who gets very little consumption will find that the user's game value is low because the user has little consumption inside the single product game. In this way, the online game developer will lose a potential promotion target because of the non-objective evaluation. In fact, such users are in large numbers, and the non-objective evaluation of these users will cause great losses to online game developers.
  • the object of the present invention is to provide a user evaluation method, device and device, which can make the data processing result more accurate and help to improve the business operation efficiency of the merchant.
  • the present invention provides a user evaluation method, including:
  • the game value of the game user is determined according to the weight of each evaluation degree.
  • the above method may further have the following features, the game behavior information includes the number of games used by the game user, the usage period of each game, the weekly activity number of each game, the monthly activity number of each game, The game user opens the frequency of each game, and the consumption information includes game information and game related consumption information of the game user.
  • the above method may further have the following feature, the based on the preset evaluability model, according to the acquired game behavior information, and the consumption information and/or the network social information, the game user is in each of the evaluations. Evaluation, including:
  • the game user's loyalty score is calculated using the loyalty calculation model.
  • the network social information includes the number of microblog fans of the game user, the number of microblogs mutual attention, and the frequency of microblog posting.
  • the above method may further have the following feature, the based on the preset evaluability model, according to the acquired game behavior information, and the consumption information and/or the network social information, the game user is in each of the evaluations. Evaluation, including:
  • Obtaining a preset influence calculation model is a weight set by the number of Weibo fans, the number of microblogs mutual attention, and the frequency of microblog postings;
  • the influence calculation model of the game user is calculated using the influence calculation model.
  • the consumption information includes comprehensive consumption information of the game user and game and game related consumption information.
  • the above method may further have the following feature, the based on the preset evaluability model, according to the acquired game behavior information, and the consumption information and/or the network social information, the game user is in each of the evaluations. Evaluation, including:
  • the consumption power score of the game user is calculated using the consumption power calculation model.
  • the user evaluation method evaluates the game value of the user according to the comprehensive information such as the game behavior information, the consumption information and the network social information of the game user, and the data processing result is more comprehensive, more objective, more accurate, and can be provided to the network.
  • Game developers have more business The basis of value, which helps online game developers to improve the accuracy of product launch and improve operational efficiency.
  • the present invention also provides a user evaluation apparatus, including:
  • a first obtaining module configured to acquire historical game behavior information of the game user
  • a second obtaining module configured to acquire consumption information and/or network social information of the game user
  • An evaluation module configured to evaluate each of the evaluation degrees of the game user according to the acquired game behavior information, and the consumption information and/or the network social information based on the preset evaluability model;
  • a determining module configured to determine a game value of the game user according to weights of the respective evaluation degrees.
  • the above device may further have the following features, the game behavior information includes the number of games used by the game user, the usage period of each game, the weekly activity number of each game, the monthly activity number of each game, The game user opens the frequency of each game, and the consumption information includes game information and game related consumption information of the game user.
  • the evaluation module includes:
  • a first weight acquisition unit configured to acquire a game amount used by the preset loyalty calculation model for the game user, a usage period of each game, a weekly activity number of each game, and each game The number of monthly activities, the frequency of opening of each game by the game user, the game of the game user, and the weight of the game-related consumption information setting;
  • a first calculating unit configured to calculate a loyalty score of the game user by using the loyalty calculation model.
  • the network social information includes the number of microblog fans of the game user, the number of microblog mutual attention, and the frequency of microblog posting.
  • the evaluation module includes:
  • the second weight obtaining unit is configured to obtain a weight of the preset influence calculation model for the number of microblog fans, the microblog mutual attention number, and the microblog posting frequency;
  • a second calculating unit configured to calculate an influence score of the game user by using the influence calculation model.
  • the above device may further have the following feature, the consumption information includes comprehensive consumption information of the game user and game and game related consumption information.
  • the evaluation module includes:
  • a third weight acquisition unit configured to acquire a weight set by the preset consumption power calculation model for the comprehensive consumption information and the game and game related consumption information
  • a third calculating unit configured to calculate a consumption power score of the game user by using the consumption power calculation model.
  • the user evaluation device of the embodiment of the present invention evaluates the game value of the user according to the comprehensive information such as the game behavior information, the consumption information and the network social information of the game user, and the data processing result is more comprehensive, more objective, more accurate, and can be provided to the network.
  • Game developers have more commercial value, which helps online game developers improve the accuracy of product launches and improve operational efficiency.
  • the present invention also provides a user evaluation apparatus comprising the user evaluation apparatus according to any of the preceding claims.
  • the user evaluation device is a computer Or server.
  • the user evaluation device of the embodiment of the invention evaluates the game value of the user according to the comprehensive information such as the game behavior information, the consumption information and the network social information of the game user, and the data processing result is more comprehensive, more objective and more accurate, and can be provided to the network.
  • Game developers have more commercial value, which helps online game developers improve the accuracy of product launches and improve operational efficiency.
  • FIG. 1 is a flowchart of a user evaluation method according to Embodiment 1 of the present invention.
  • FIG. 2 is a structural block diagram of a user evaluation apparatus according to Embodiment 2 of the present invention.
  • FIG. 3 is a structural block diagram of a user evaluation device according to Embodiment 3 of the present invention.
  • FIG. 1 is a flowchart of a user evaluation method according to Embodiment 1 of the present invention. As shown in FIG. 1 , in this embodiment, the user evaluation method may include the following steps:
  • Step S101 acquiring historical game behavior information of the game user
  • the history game behavior information refers to the game behavior information of the past set time period.
  • the set time period can be within the past month, within three months, within six months, and the like.
  • the game behavior information may include the number of games used by the game user, the usage period of each game, the weekly activity number of each game, the monthly activity number of each game, the opening frequency of each game by the game user, and the like.
  • the weekly activity of this game is equal to the sum of the number of sub-actives of m times.
  • the principle of the monthly active number is the same as the weekly active number, and will not be described here.
  • Step S102 acquiring consumption information and/or network social information of the game user
  • the consumption information may include comprehensive consumption information of the game user, game of the game user, and game related consumption information.
  • the comprehensive consumption information of the game user refers to the total amount of online consumption of the game user during the set time period.
  • the game user's game and game related consumption information refers to the game user's consumption on the game and the consumption around the game. Among them, the consumption around the game refers to the consumption based on the game.
  • the social information of the network may include the number of microblog fans of the game user, the number of mutual attention of the microblog, the frequency of posting the microblog, and the like. Online social information can also be information related to WeChat, community websites, and gaming platforms.
  • the obtained game behavior information, consumption information and network social information are all used as the basis for evaluating the value of the user's game, which can make the evaluation of the user's game value more comprehensive, more objective and more accurate.
  • Step S103 based on the preset evaluability model, evaluating the evaluation degree of the game user according to the acquired game behavior information, and the consumption information and/or the network social information;
  • the evaluation degree calculation model can be reasonably determined according to actual conditions.
  • three evaluation degrees that is, loyalty, influence, and consumption power can be designed.
  • loyalty can be used to measure the extent to which game users are keen on online games.
  • Influence can be used to measure the appeal and impact of game users on other games in online games.
  • Consumer power can be used to measure the spending power of game users on online games.
  • other evaluative models may be designed as needed, and the evaluative models need to be able to objectively and accurately evaluate the game users by using the acquired game behavior information, consumption information, and network social information.
  • step S104 the game value of the game user is determined according to the weight of each evaluation degree.
  • each evaluation degree is used as a measure factor of the game value component, and each evaluation degree is integrated, and different weights are set for each evaluation degree according to actual needs, so that the game value points are more objectively and accurately reflected.
  • the value of the user's game is used as a measure factor of the game value component, and each evaluation degree is integrated, and different weights are set for each evaluation degree according to actual needs, so that the game value points are more objectively and accurately reflected.
  • the game value is obtained according to the comprehensive influence of each evaluation degree, and each evaluation degree is calculated based on the game user's game behavior information, consumption information and network social information, therefore, It can reflect the user's game value more comprehensively, objectively and accurately.
  • the game value may be equal to the cumulative sum of the products of the respective evaluation degrees and the corresponding weights.
  • the game behavior information may include the number of game games used by the game user, the usage period of each game, the weekly activity number of each game, the monthly activity number of each game, and the game user's game for each game.
  • the frequency of opening, the consumption information includes the game user's game and game related consumption information.
  • the foregoing step S103 may include: acquiring a preset loyalty calculation model for the number of games used by the game user, a usage period of each game, a weekly activity number of each game, and each paragraph.
  • the loyalty calculation model is used to calculate the loyalty score of the game user, wherein the loyalty score can be Equal to the number of games used by game users, the usage period of each game, the weekly activity of each game, the monthly activity of each game, the frequency of opening each game by the game user, the game user's game and game related consumption.
  • the network social information may include the number of microblog fans of the game user, the number of microblogs mutual attention, and the frequency of microblog posting.
  • the foregoing step S103 may include: acquiring a preset influence calculation model as the weight of the microblog fans, the microblog mutual attention number, and the microblog posting frequency; using the influence calculation model to calculate The influence score of the game user, wherein the influence score may be equal to the sum of the sum of the number of microblog fans of the game user, the number of mutual attention of the microblog, and the frequency of the microblog postings respectively.
  • the consumption information may include comprehensive consumption information and tour of the game user. Play and game related consumer information.
  • the foregoing step S103 may include: acquiring a preset consumption power calculation model for the comprehensive consumption information and the weight set by the game and the game related consumption information; and calculating the consumption power score of the game user by using the consumption power calculation model; Wherein, the power consumption score may be equal to an accumulated sum of the product of the integrated consumption information and the game and game related consumption information and their weights.
  • S2 is equal to the life cycle of each game (the data is dynamic). If a player does not log in to the game within 180 days, then the player is considered to have ended for a certain period of the game. However, the total period does not end: for example, the player will use from 2013-1-1 (indicating January 1, 2013) to 2014-1-1, the last time is: 2014-1-1, then 2015-5-5 and re-played to this day, then this game for this player, the cycle is from 2013-1-1 to 2014-1-1 plus 2015-5-5 so far, calculated by month unit.
  • the loyalty score S is calculated by calculation.
  • Y1 The number of fans on Weibo, such as 200000;
  • Y3 The frequency of posting on Weibo, for example: posting 20 times a month (months);
  • Y Y1*Y1 weight coefficient +Y2*Y2 weight coefficient +Y3*Y3 weight coefficient.
  • X1 Accumulate the transaction data in all the consumption records of the game, for example, 1000 yuan;
  • X2 transaction accumulation of game peripheral consumption records, for example, 2000 yuan;
  • X x1*x1 weighting factor +x2*x2 weighting factor.
  • the weight coefficients of the above data are dynamically configured.
  • game value score loyalty score S*S weight coefficient + influence score Y*Y weight coefficient + consumption power score X * X weight coefficient.
  • the user evaluation method evaluates the game value of the user according to the comprehensive information such as the game behavior information, the consumption information and the network social information of the game user, and the data processing result is more comprehensive, more objective, more accurate, and can be provided to the network.
  • Game developers have more commercial value, which helps online game developers improve the accuracy of product launches and improve operational efficiency.
  • FIG. 2 is a structural block diagram of a user evaluation apparatus according to Embodiment 2 of the present invention.
  • the user evaluation device of Fig. 2 can be used to implement the user evaluation method in the foregoing embodiments of the present invention.
  • the principle description in the foregoing embodiment of the user evaluation method of the present invention is also applicable to the user evaluation apparatus described below.
  • the user evaluation apparatus 200 may include a first acquisition module. 210.
  • the first obtaining module 210, the second obtaining module 220, the evaluating module 230, and the determining module 240 may be sequentially connected.
  • the first obtaining module 210 is configured to acquire historical game behavior information of the game user.
  • the second obtaining module 220 is configured to acquire consumption information and/or network social information of the game user.
  • the evaluation module 230 is configured to evaluate each of the evaluation degrees of the game user according to the acquired game behavior information, and the consumption information and/or the network social information based on the preset evaluability model.
  • the determining module 240 is configured to determine the game value of the game user according to the weight of each evaluation degree.
  • the game value may be equal to the cumulative sum of the products of the respective evaluation degrees and the corresponding weights.
  • the evaluation module 230 may include a connected first weight acquisition unit and a first calculation unit.
  • the game behavior information may include the number of games used by the game user, the usage period of each game, the weekly activity number of each game, the monthly activity number of each game, the opening frequency of each game by the game user, and the consumption information including the game.
  • the first weight acquisition unit is configured to acquire a preset loyalty calculation model for the number of game games used by the game user, a usage period of each game, a weekly activity number of each game, a monthly activity number of each game, and a game user pair.
  • the weight of each game, the game user's game and the game-related consumption information setting weight; the first calculation unit is used to calculate the game user's loyalty score using the loyalty calculation model.
  • the loyalty score may be equal to the number of games used by the game user, the usage period of each game, the weekly activity of each game, the monthly activity of each game, the opening frequency of each game by the game user, and the game.
  • the evaluation module 230 may further include a connected second weight acquisition unit and a second calculation unit.
  • Online social information can include the number of Weibo fans of game users, micro Bo pays attention to the number of posts and microblog posts.
  • the second weight obtaining unit is configured to obtain a weight of the preset influence calculation model for the number of microblog fans, the microblog mutual attention number, and the microblog posting frequency; the second calculating unit is configured to calculate the game by using the influence calculation model.
  • the influence score of the user wherein the influence score may be equal to the sum of the product of the number of Weibo fans of the game user, the number of microblogs mutual attention, and the frequency of each microblog posting frequency and its weight.
  • the evaluation module 230 may further include a connected third weight acquisition unit and a third calculation unit.
  • the consumption information may include comprehensive consumption information of the game user and game and game related consumption information.
  • the third weight obtaining unit is configured to obtain a weight set by the preset consumption power calculation model for the comprehensive consumption information and the game and the game related consumption information; and the third calculating unit is configured to calculate the consumption power score of the game user by using the consumption power calculation model, Wherein, the consumption power score may be equal to the sum of the sum of the comprehensive consumption information and the game and the game-related consumption information respectively.
  • the user evaluation device of the embodiment of the present invention evaluates the game value of the user according to the comprehensive information such as the game behavior information, the consumption information and the network social information of the game user, and the data processing result is more comprehensive, more objective, more accurate, and can be provided to the network.
  • Game developers have more commercial value, which helps online game developers improve the accuracy of product launches and improve operational efficiency.
  • FIG. 3 is a structural block diagram of a user evaluation device according to Embodiment 3 of the present invention. As shown in FIG. 3, in the embodiment, the user evaluation device 200 may be included in the user evaluation device 300.
  • the user evaluation device 200 may be configured to acquire historical game behavior information of the game user; acquire consumption information and/or network social information of the game user; and based on the preset evaluability model, according to the acquired game behavior information, and the consumption information and / or network social information, for game users Each of the evaluation degrees is evaluated; and the game value of the game user is determined according to the weight of each evaluation degree.
  • the user evaluation device 300 may be a computer, a server, or the like.
  • the user evaluation device of the embodiment of the present invention includes a user evaluation device, and evaluates the game value of the user according to comprehensive information such as game behavior information, consumption information, and network social information of the game user, and the data processing result is more comprehensive, more objective, and more accurate. It can provide online game developers with more commercial value, which helps online game developers to improve the accuracy of product launch and improve operational efficiency.

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Abstract

本申请涉及一种用户评价方法、装置及设备。其中，用户评价方法包括：获取游戏用户的历史游戏行为信息；获取所述游戏用户的消费信息和/或网络社交信息；基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价；根据各个评价度的权重，确定所述游戏用户的游戏价值。本发明数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。

Description

用户评价方法、装置及设备 技术领域
本发明涉及计算机领域，尤其涉及一种用户评价方法、装置及设备。
背景技术
当今时代是一个网络时代，在人们生活的各个领域、各个方面，网络无处不在。网络给人们的生活带来了变革，与此同时，也催生出了许多新兴的行业。
网络游戏行业就是网络时代的产物之一。网络游戏已经成为当今社会上一种主要的娱乐方式，特别是对上班族来说。为了寻找潜在的游戏玩家(以下也称为游戏用户)，推广网络游戏，网络游戏开发者需要对用户的游戏价值进行评价。这里，用户的游戏价值是指向用户推广网络游戏的价值。用户的游戏价值越高，向用户推广网络游戏的价值就越高。游戏价值越高的用户接受推广的网络游戏的可能性越大。因此，用户的游戏价值评价分数对于网络游戏开发者而言具有重大的商业价值。
相关技术中，通过游戏用户在某款游戏内部的消费情况和行为情况来评价用户的游戏价值。这种只依据用户在单品游戏的消费和行为情况评价用户游戏价值的方式显然不够客观。比如，用户在一个单品游戏内部消费很少，但是，该用户在另一款类似的游戏中消费很多。显然，这个用户是个游戏价值较高的用户，但是获得很少消费的网络游戏开发者却会因为该用户在其单品游戏内部的消费很少的情况认定该用户的游戏价值低，这 样，该网络游戏开发者就会因不客观的评价而失去一个很有潜力的推广对象。实际上，这样的用户是大量存在的，对这些用户的不客观评价会给网络游戏开发者造成很大损失。
发明内容
本发明的目的在于提供一种用户评价方法、装置及设备，能够使数据处理结果更加准确，有助于提高商家经营效益。
为实现上述目的，本发明提出了一种用户评价方法，包括：
获取游戏用户的历史游戏行为信息；
获取所述游戏用户的消费信息和/或网络社交信息；
基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价；
根据各个评价度的权重，确定所述游戏用户的游戏价值。
进一步地，上述方法还可具有以下特点，所述游戏行为信息包括所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次，所述消费信息包括所述游戏用户的游戏及游戏相关消费信息。
进一步地，上述方法还可具有以下特点，所述基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价，包括：
获取预设的忠诚度计算模型为所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用 户对每款游戏的打开频次、所述游戏用户的游戏及游戏相关消费信息设置的权重；
利用所述忠诚度计算模型计算所述游戏用户的忠诚度分值。
进一步地，上述方法还可具有以下特点，所述网络社交信息包括所述游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次。
进一步地，上述方法还可具有以下特点，所述基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价，包括：
获取预设的影响力计算模型为所述微博粉丝数量、微博相互关注数、微博发帖频次设置的权重；
利用所述影响力计算模型计算所述游戏用户的影响力分值。
进一步地，上述方法还可具有以下特点，所述消费信息包括所述游戏用户的综合消费信息和游戏及游戏相关消费信息。
进一步地，上述方法还可具有以下特点，所述基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价，包括：
获取预设的消费力计算模型为所述综合消费信息和所述游戏及游戏相关消费信息设置的权重；
利用所述消费力计算模型计算所述游戏用户的消费力分值。
本发明实施例的用户评价方法，依据游戏用户的游戏行为信息、消费信息和网络社交信息等全面信息对用户的游戏价值进行评价，数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业 价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。
为实现上述目的，本发明还提出了一种用户评价装置，包括：
第一获取模块，用于获取游戏用户的历史游戏行为信息；
第二获取模块，用于获取所述游戏用户的消费信息和/或网络社交信息；
评价模块，用于基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价；
确定模块，用于根据各个评价度的权重，确定所述游戏用户的游戏价值。
进一步地，上述装置还可具有以下特点，所述游戏行为信息包括所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次，所述消费信息包括所述游戏用户的游戏及游戏相关消费信息。
进一步地，上述装置还可具有以下特点，所述评价模块包括：
第一权重获取单元，第一权重获取单元用于获取预设的忠诚度计算模型为所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次、所述游戏用户的游戏及游戏相关消费信息设置的权重；
第一计算单元，用于利用所述忠诚度计算模型计算所述游戏用户的忠诚度分值。
进一步地，上述装置还可具有以下特点，所述网络社交信息包括所述游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次。
进一步地，上述装置还可具有以下特点，所述评价模块包括：
第二权重获取单元，第二权重获取单元用于获取预设的影响力计算模型为所述微博粉丝数量、微博相互关注数、微博发帖频次设置的权重；
第二计算单元，用于利用所述影响力计算模型计算所述游戏用户的影响力分值。
进一步地，上述装置还可具有以下特点，所述消费信息包括所述游戏用户的综合消费信息和游戏及游戏相关消费信息。
进一步地，上述装置还可具有以下特点，所述评价模块包括：
第三权重获取单元，第三权重获取单元用于获取预设的消费力计算模型为所述综合消费信息和所述游戏及游戏相关消费信息设置的权重；
第三计算单元，用于利用所述消费力计算模型计算所述游戏用户的消费力分值。
本发明实施例的用户评价装置，依据游戏用户的游戏行为信息、消费信息和网络社交信息等全面信息对用户的游戏价值进行评价，数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。
为实现上述目的，本发明还提出了一种用户评价设备，包括前述任一项所述的用户评价装置。
进一步地，上述设备还可具有以下特点，所述用户评价设备为计算机 或服务器。
本发明实施例的用户评价设备，依据游戏用户的游戏行为信息、消费信息和网络社交信息等全面信息对用户的游戏价值进行评价，数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。
附图说明
图1为本发明实施例一中用户评价方法的流程图。
图2为本发明实施例二中用户评价装置的结构框图。
图3为本发明实施例三中用户评价设备的结构框图。
具体实施方式
以下结合附图对本发明的原理和特征进行描述，所举实施例只用于解释本发明，并非用于限定本发明的范围。对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，根据本发明精神所获得的所有实施例，都属于本发明的保护范围。
图1为本发明实施例一中用户评价方法的流程图。如图1所示，本实施例中，用户评价方法可以包括如下步骤：
步骤S101，获取游戏用户的历史游戏行为信息；
其中，历史游戏行为信息是指过去的设定时间段的游戏行为信息。例如，设定时间段可以是过去的一个月内、三个月内、半年内等等。
其中，游戏行为信息可以包括游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次等等。
这里举例对每款游戏的周活跃数和月活跃数的含义进行说明。
周活跃数：假设用户一周内登录某款游戏的次数为m，每次登录的在线时间为t(分钟)，设置三个阈值T1、T2、T3，T1＜T2＜T3，如果0＜t＜T1，则每次的活跃数(简称次活跃数)n＝n1；如果T1≤t＜T2，则n＝n2；如果T2≤t＜T3，则n＝n3；如果t≥T3，则n＝n4，n1＜n2＜n3＜n4。该款游戏的周活跃数等于m次的次活跃数之和。例如，T1、T2、T3分别等于10、30、60，n1、n2、n3、n4分别等于0.5、1、1.5、2，则如果0＜t＜10，次活跃数n＝0.5；则如果10≤t＜30，n＝1；如果30≤t＜60，n＝1.5；如果t≥60，n＝2。
月活跃数的原理同周活跃数一样，此处不再赘述。
步骤S102，获取游戏用户的消费信息和/或网络社交信息；
其中，消费信息可以包括游戏用户的综合消费信息、游戏用户的游戏及游戏相关消费信息等。游戏用户的综合消费信息是指游戏用户在所述设定时间段内的网上消费总数。游戏用户的游戏及游戏相关消费信息是指游戏用户在游戏上的消费以及在游戏周边的消费。其中，在游戏周边的消费是指基于游戏产生的消费。以游戏“魔兽世界”为例，购买“魔兽世界”的角色、手办(玩偶)/玩具，购买与游戏“魔兽世界”相关的T恤，购买与游戏“魔兽世界”相关的书籍，购买与游戏“魔兽世界”相关的饰品、杯具、钥匙扣、打火机等等，都属于游戏“魔兽世界”的周边消费。
其中，网络社交信息可以包括游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次等。网络社交信息还可以是与微信、社区网站、游戏平台相关的信息。
获取的游戏行为信息、消费信息和网络社交信息都作为评价用户游戏价值的依据，这样可以使得对用户的游戏价值的评价更加全面、更加客观、更加准确。
步骤S103，基于预设的评价度模型，根据获取的游戏行为信息、以及消费信息和/或网络社交信息，对游戏用户在各个评价度进行评价；
评价度计算模型可以根据实际情况合理确定。例如，在本发明实施例中，可以设计三个评价度，即忠诚度、影响力、消费力。其中，忠诚度可以用于衡量游戏用户热衷于网络游戏的程度。影响力可以用于衡量游戏用户在网络游戏方面对于其他用户的号召力和影响程度。消费力可以用于衡量游戏用户在网络游戏上的消费能力。当然，在本发明其他实施例中，可以根据需要设计其他的评价度模型，这些评价度模型需要能够利用获取的游戏行为信息、消费信息和网络社交信息对游戏用户进行客观、准确的评价。
步骤S104，根据各个评价度的权重，确定游戏用户的游戏价值。
在本发明实施例中，每个评价度作为游戏价值分的一方面衡量因素，综合各个评价度，并根据实际需要为各评价度设置不同的权重，以使游戏价值分更加客观、准确地反映用户的游戏价值。
游戏价值是根据各个评价度的综合影响处理得到的，而各个评价度的计算依据是游戏用户的游戏行为信息、消费信息和网络社交信息，因此， 能够更加全面、客观、准确地反映用户的游戏价值。
其中，游戏价值可以等于各评价度与相应权重乘积的累加和。
在本发明实施例中，游戏行为信息可以包括游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次，消费信息包括游戏用户的游戏及游戏相关消费信息。
在本发明实施例中，前述的步骤S103可以包括：获取预设的忠诚度计算模型为所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次、游戏用户的游戏及游戏相关消费信息设置的权重；利用忠诚度计算模型计算游戏用户的忠诚度分值，其中，忠诚度分值可以等于游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次、游戏用户的游戏及游戏相关消费信息各自与其权重的乘积的累加之和。
在本发明实施例中，网络社交信息可以包括游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次。
在本发明实施例中，前述的步骤S103可以包括：获取预设的影响力计算模型为所述微博粉丝数量、微博相互关注数、微博发帖频次设置的权重；利用影响力计算模型计算游戏用户的影响力分值，其中，影响力分值可以等于游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次各自与其权重的乘积的累加之和。
在本发明实施例中，消费信息可以包括游戏用户的综合消费信息和游 戏及游戏相关消费信息。
在本发明实施例中，前述的步骤S103可以包括：获取预设的消费力计算模型为综合消费信息和游戏及游戏相关消费信息设置的权重；利用消费力计算模型计算游戏用户的消费力分值，其中，消费力分值可以等于综合消费信息和所述游戏及游戏相关消费信息各自与其权重的乘积的累加之和。
下面举例说明游戏分值的计算过程：
假设已经获取到计算所需的数据。
(1)计算忠诚度分值S:
S1等于游戏用户A同时使用游戏的数目。假设用户A同时玩3款手游，那么s1＝3。
S2等于每款游戏的使用周期(该数据是动态的)。如果一个玩家在180天内没有登陆游戏，则认为这个玩家对于此款游戏的某个周期结束。但是总周期没有结束：比如玩家从2013-1-1(表示2013年1月1日)日到2014-1-1日都有使用，最后一次使用时间为：2014-1-1日，然后在2015-5-5日又重新玩到至今，那么此款游戏对于这个玩家来说，周期就是从2013-1-1日到2014-1-1加上2015-5-5至今，以月为计算单位。
S2＝12+9＝21(月)
S3＝周活跃数：180
S4＝月活跃数：500
则S＝S1*S1权重系数+S2*S2权重系数+S3*S3权重系数+S4*S4权重系数，其中，符号“*”为乘法运算符，表示“乘以”。
通过计算得出忠诚度分值S。
(2)计算影响力分值Y
Y1:微博的粉丝数量，例如200000；
Y2:微博相互关注数，例如30000；
Y3:微博发帖频次，例如:一个月发帖20次(月为单位)；
Y＝Y1*Y1权重系数+Y2*Y2权重系数+Y3*Y3权重系数。
(3)计算消费力分值X
X1＝游戏所有消费记录中的交易数据累加，例如1000元；
X2＝游戏周边消费记录的交易累加，例如2000元；
X＝x1*x1权重系数+x2*x2权重系数。
以上各数据的权重系数是动态配置的。
最后计算游戏价值分，游戏价值分＝忠诚度分值S*S权重系数+影响力分值Y*Y权重系数+消费力分值X*X权重系数。
本发明实施例的用户评价方法，依据游戏用户的游戏行为信息、消费信息和网络社交信息等全面信息对用户的游戏价值进行评价，数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。
图2为本发明实施例二中用户评价装置的结构框图。图2中的用户评价装置可以用于实施本发明前述实施例中的用户评价方法。本发明前述用户评价方法实施例中的原理说明也适用于下述的用户评价装置。
如图2所示，本实施例中，用户评价装置200可以包括第一获取模块 210、第二获取模块220、评价模块230和确定模块240。第一获取模块210、第二获取模块220、评价模块230和确定模块240可以顺次相连。
其中，第一获取模块210用于获取游戏用户的历史游戏行为信息。第二获取模块220用于获取游戏用户的消费信息和/或网络社交信息。评价模块230用于基于预设的评价度模型，根据获取的游戏行为信息、以及消费信息和/或网络社交信息，对游戏用户在各个所述评价度进行评价。确定模块240用于根据各个评价度的权重，确定游戏用户的游戏价值。
其中，游戏价值可以等于各评价度与相应权重乘积的累加和。
在本发明实施例中，评价模块230可以包括相连的第一权重获取单元和第一计算单元。游戏行为信息可以包括游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次，消费信息包括游戏用户的游戏及游戏相关消费信息。第一权重获取单元用于获取预设的忠诚度计算模型为游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次、游戏用户的游戏及游戏相关消费信息设置的权重；第一计算单元用于利用忠诚度计算模型计算游戏用户的忠诚度分值。其中，忠诚度分值可以等于游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、游戏用户对每款游戏的打开频次、游戏用户的游戏及游戏相关消费信息各自与其权重的乘积的累加之和。
在本发明实施例中，评价模块230还可以包括相连的第二权重获取单元和第二计算单元。网络社交信息可以包括游戏用户的微博粉丝数量、微 博相互关注数、微博发帖频次。第二权重获取单元用于获取预设的影响力计算模型为所述微博粉丝数量、微博相互关注数、微博发帖频次设置的权重；第二计算单元用于利用影响力计算模型计算游戏用户的影响力分值，其中，影响力分值可以等于游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次各自与其权重的乘积的累加之和。
在本发明实施例中，评价模块230还可以包括相连的第三权重获取单元和第三计算单元。消费信息可以包括游戏用户的综合消费信息和游戏及游戏相关消费信息。第三权重获取单元用于获取预设的消费力计算模型为综合消费信息和游戏及游戏相关消费信息设置的权重；第三计算单元用于利用消费力计算模型计算游戏用户的消费力分值，其中，消费力分值可以等于综合消费信息和游戏及游戏相关消费信息各自与其权重的乘积的累加之和。
本发明实施例的用户评价装置，依据游戏用户的游戏行为信息、消费信息和网络社交信息等全面信息对用户的游戏价值进行评价，数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。
图3为本发明实施例三中用户评价设备的结构框图。如图3所示，本实施例中，用户评价设备300中可以包括用户评价装置200。
其中，用户评价装置200可以用于获取游戏用户的历史游戏行为信息；获取游戏用户的消费信息和/或网络社交信息；基于预设的评价度模型，根据获取的游戏行为信息、以及消费信息和/或网络社交信息，对游戏用户在 各个所述评价度进行评价；根据各个评价度的权重，确定游戏用户的游戏价值。
其中，用户评价设备300可以是计算机、服务器等。
本发明实施例的用户评价设备中包括用户评价装置，依据游戏用户的游戏行为信息、消费信息和网络社交信息等全面信息对用户的游戏价值进行评价，数据处理结果更加全面、更加客观、更加准确，能够提供给网络游戏开发者更有商业价值的依据，从而帮助网络游戏开发者提升产品投放的精准度，提高经营效益。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (16)

  1. 一种用户评价方法，其特征在于，包括：
    获取游戏用户的历史游戏行为信息；
    获取所述游戏用户的消费信息和/或网络社交信息；
    基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价；
    根据各个评价度的权重，确定所述游戏用户的游戏价值。
  2. 根据权利要求1所述的用户评价方法，其特征在于，所述游戏行为信息包括所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次，所述消费信息包括所述游戏用户的游戏及游戏相关消费信息。
  3. 根据权利要求2所述的用户评价方法，其特征在于，所述基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价，包括：
    游戏用户游戏用户游戏用户获取预设的忠诚度计算模型为所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次、所述游戏用户的游戏及游戏相关消费信息设置的权重；
    利用所述忠诚度计算模型计算所述游戏用户的忠诚度分值。
  4. 根据权利要求1所述的用户评价方法，其特征在于，所述网络社交信息包括所述游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次。
  5. 根据权利要求4所述的用户评价方法，其特征在于，所述基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价，包括：
    获取预设的影响力计算模型为所述微博粉丝数量、微博相互关注数、微博发帖频次设置的权重；
    利用所述影响力计算模型计算所述游戏用户的影响力分值。
  6. 根据权利要求1所述的用户评价方法，其特征在于，所述消费信息包括所述游戏用户的综合消费信息和游戏及游戏相关消费信息。
  7. 根据权利要求6所述的用户评价方法，其特征在于，所述基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价，包括：
    获取预设的消费力计算模型为所述综合消费信息和所述游戏及游戏相关消费信息设置的权重；
    利用所述消费力计算模型计算所述游戏用户的消费力分值。
  8. 一种用户评价装置，其特征在于，包括：
    第一获取模块，用于获取游戏用户的历史游戏行为信息；
    第二获取模块，用于获取所述游戏用户的消费信息和/或网络社交信息；
    评价模块，用于基于预设的评价度模型，根据获取的所述游戏行为信息、以及消费信息和/或网络社交信息，对所述游戏用户在各个所述评价度进行评价；
    确定模块，用于根据各个评价度的权重，确定所述游戏用户的游戏价 值。
  9. 根据权利要求8所述的用户评价装置，其特征在于，所述游戏行为信息包括所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次，所述消费信息包括所述游戏用户的游戏及游戏相关消费信息。
  10. 根据权利要求9所述的用户评价装置，其特征在于，所述评价模块包括：
    第一权重获取单元，用于获取预设的忠诚度计算模型为所述游戏用户使用的游戏款数、每款游戏的使用周期、每款游戏的周活跃数、每款游戏的月活跃数、所述游戏用户对每款游戏的打开频次、所述游戏用户的游戏及游戏相关消费信息设置的权重；
    第一计算单元，用于利用所述忠诚度计算模型计算所述游戏用户的忠诚度分值。
  11. 根据权利要求8所述的用户评价装置，其特征在于，所述网络社交信息包括所述游戏用户的微博粉丝数量、微博相互关注数、微博发帖频次。
  12. 根据权利要求11所述的用户评价装置，其特征在于，所述评价模块包括：
    第二权重获取单元，用于获取预设的影响力计算模型为所述微博粉丝数量、微博相互关注数、微博发帖频次设置的权重；
    第二计算单元，用于利用所述影响力计算模型计算所述游戏用户的影响力分值。
  13. 根据权利要求8所述的用户评价装置，其特征在于，所述消费信息包括所述游戏用户的综合消费信息和游戏及游戏相关消费信息。
  14. 根据权利要求13所述的用户评价装置，其特征在于，所述评价模块包括：
    第三权重获取单元，用于获取预设的消费力计算模型为所述综合消费信息和所述游戏及游戏相关消费信息设置的权重；
    第三计算单元，用于利用所述消费力计算模型计算所述游戏用户的消费力分值。
  15. 一种用户评价设备，其特征在于，包括权利要求8至14任一项所述的用户评价装置。
  16. 根据权利要求15所述的用户评价设备，其特征在于，所述用户评价设备为计算机或服务器。
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