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    • 1. 发明申请
    • INFERRING USER DEMOGRAPHIC INFORMATION FROM RATINGS
    • 从评级中引入用户人口统计信息
    • WO2014093618A3
    • 2014-10-23
    • PCT/US2013074662
    • 2013-12-12
    • THOMSON LICENSINGIOANNIDIS STRATISWEINSBERG UDIBHAGAT SMRITI
    • IOANNIDIS STRATISWEINSBERG UDIBHAGAT SMRITI
    • G06Q10/00
    • G06Q30/0204G06Q10/06315G06Q30/0282
    • Existing recommendation systems leverage user social and demographic information, e.g., age, gender and political affiliation, to personalize content and make recommendations. However, users do not volunteer this information due to privacy concerns or to the lack of initiative in filling out their profile information. The current methods and apparatus provide principles in which the system may learn the private attribute for those users who do not voluntarily disclose them. In an exemplary embodiment, the system receives ratings for items, such as movies, for example, that may be used by a recommendation system. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting. Such a system can be carried out using significantly fewer rated items than previously proposed static inference methods. The system functions effectively without sacrificing the quality of the regular recommendations made to the user.
    • 现有的推荐系统利用用户社交和人口统计信息(例如年龄,性别和政治隶属关系)来个性化内容并提出建议。 但是,由于隐私问题或缺乏主动填写个人资料信息,用户不会自愿提供这些信息。 当前的方法和装置提供了原理,其中系统可以为那些不愿意公开他们的用户学习私人属性。 在示例性实施例中,系统接收例如可由推荐系统使用的项目(例如电影)的评级。 本发明的配置基于在主动学习环境中的贝叶斯矩阵分解的新用法。 这样的系统可以使用比先前提出的静态推断方法少得多的额定项目来执行。 该系统可以有效运行,而不会影响向用户提供的常规建议的质量。
    • 5. 发明申请
    • METHOD AND SYSTEM FOR DESIGNING A DATA MARKET EXPERIMENT
    • 用于设计数据市场实验的方法和系统
    • WO2014120348A3
    • 2015-09-03
    • PCT/US2013075468
    • 2013-12-16
    • THOMSON LICENSING
    • IOANNIDIS STRATISHOREL THIBAUT Y
    • G06Q10/00
    • G06Q10/06313G06Q10/06G06Q30/0201G06Q50/22
    • An apparatus and a method for designing a data market experiment given a fixed budget and a set of potential subjects for the experiment are described. An experimenter conducts an online survey, for example, or a test on human subject, or any other kind of experiment through which it collects data, and can incentivize the participation of subjects in the experiment through monetary compensation. The experimenter observes some publicly known information about the subjects, as well as the money each potential subject requests to participate in the experiment. Based on this information, the method determines which users to pay, and how much, to participate in the experiment. The method views experimental design in a strategic setting, by studying mechanism design issues, such as incentivizing users to report a truthful value for their data. The method has the following properties of being budget feasible, computationally tractable, nearly-optimal, and truthful in that the subjects have no incentive to declare desired compensations that are untruthful.
    • 描述了给定固定预算的数据市场实验的设备和方法以及用于实验的一组潜在主体。 例如,实验者进行在线调查,或对人体科目进行在线调查,或进行其他收集数据的其他实验,并通过货币补偿激励学科参与实验。 实验者观察一些关于主题的公开信息,以及每个潜在主体要求参与实验的资金。 根据这些信息,该方法确定哪些用户付费,以及多少参与实验。 该方法通过研究机制设计问题,如激励用户为其数据报告真实价值,从而在战略环境中观察实验设计。 该方法具有以下属性:预算可行性,计算易处理性,接近最优性和真实性,因为受试者没有动机申报不合需要的补偿。