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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。
    • 7. 发明申请
    • PROPOSING OBJECTS TO A USER TO EFFICIENTLY DISCOVER DEMOGRAPHICS FROM ITEM RATINGS
    • 向用户提出对象从物品等级有效发现人口统计学的目标
    • WO2014093621A2
    • 2014-06-19
    • PCT/US2013/074665
    • 2013-12-12
    • THOMSON LICENSINGIOANNIDIS, StratisWEINSBERG, UdiBHAGAT, Smriti
    • IOANNIDIS, StratisWEINSBERG, UdiBHAGAT, Smriti
    • G06Q30/0204G06N5/04G06N7/005G06N99/005G06Q30/0241G06Q30/0278G06Q30/0282
    • The current methods and apparatus provide a system that learns a private attribute, such as gender, based on at least one iteration of presenting an item to a user and receiving ratings from the user for this item. In an exemplary embodiment, the system may solicit ratings for strategically selected items, such as movies for example, and then infers the user's gender. Based on the assessed confidence in the demographic selected, the system may repeat the selection, presentation and ratings of another item. The proposed system can strategically select the sequence of items that are presented to the user for a rating. By selecting the next item to be rated based on a maximum posterior probability confidence, a demographic with a certain threshold of confidence can be inferred. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting. Such a system is shown to be feasible and can be carried out using significantly fewer rated items than previously proposed static inference methods.
    • 目前的方法和装置提供了一种系统,其基于至少一次向用户呈现项目的迭代来学习诸如性别的私人属性,并且从用户接收对该项目的评级。 在示例性实施例中,系统可以针对诸如电影的战略选择的项目征求评级,然后推断用户的性别。 根据对所选人口的评估信心,系统可能会重复对另一项目的选择,呈现和评级。 所提出的系统可以策略地选择呈现给用户的评级的项目的顺序。 通过基于最大后验概率置信选择要评级的下一个项目,可以推断具有一定阈值的人口统计学。 本发明的布置基于在主动学习设置中贝叶斯矩阵分解的新颖使用。 这样的系统被证明是可行的,并且可以使用比先前提出的静态推理方法明显更少的额定项目来执行。
    • 8. 发明申请
    • INFERRING USER DEMOGRAPHIC INFORMATION FROM RATINGS
    • 从评分中输入用户人口统计信息
    • WO2014093618A2
    • 2014-06-19
    • PCT/US2013/074662
    • 2013-12-12
    • THOMSON LICENSINGIOANNIDIS, StratisWEINSBERG, UdiBHAGAT, Smriti
    • IOANNIDIS, StratisWEINSBERG, UdiBHAGAT, Smriti
    • G06Q10/10
    • 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会和人口统计信息,例如年龄,性别和政治隶属关系,个性化内容和提出建议。 但是,由于隐私问题或用户填写个人资料信息缺乏主动性,用户不会自愿提供此信息。 目前的方法和设备提供了系统可以为那些不自愿披露他们的用户学习私有属性的原理。 在示例性实施例中,系统接收诸如可由推荐系统使用的诸如电影的项目的等级。 本发明的布置基于在主动学习设置中贝叶斯矩阵分解的新颖使用。 这样的系统可以使用比以前提出的静态推理方法少得多的额定项目来执行。 系统功能有效,不会牺牲对用户的定期建议的质量。