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    • 5. 发明申请
    • AUTOMATIC DETERMINATION OF GENRE-SPECIFIC RELEVANCE OF RECOMMENDATIONS IN A SOCIAL NETWORK
    • 自动确定社会网络中建议的特定相关性
    • US20140136621A1
    • 2014-05-15
    • US14131510
    • 2012-07-13
    • Jan KorstMauro BarbieriServerius Petrus Paulus Pronk
    • Jan KorstMauro BarbieriServerius Petrus Paulus Pronk
    • H04L29/08
    • H04L67/306G06Q30/0631G06Q50/01
    • The present invention relates to an operating method of operating a recommender system, a filtering apparatus (260) for a recommender system (200), a recommender system and a corresponding computer program. An idea of the invention is to automatically learn for a user A in a social network, which recommendations of contacts of user A, who are also members of the social network, are relevant with respect to a genre into which user A is interested in. A learning algorithm is used to interpret feedback from user A in response to receiving recommendations from his/her contacts. Thereby, for each combination of a contact and a genre, a relevance-taste index can be determined. The determined relevance-taste index is subjected to a filter. Only such recommendations are provided to user A, whose associated relevance-taste indices fulfil a filtering criterion. Thereby, the amount of irrelevant recommendations submitted to user A can be significantly reduced.
    • 本发明涉及操作推荐系统的操作方法,用于推荐系统(200)的过滤装置(260),推荐系统和对应的计算机程序。 本发明的思想是在社交网络中自动学习用户A,用户A的联系人也是社交网络的成员,与用户A感兴趣的类型相关。 一个学习算法用于解释用户A响应于他/她的联系人的接收建议的反馈。 因此,对于联系人和类型的每个组合,可以确定相关性味道指数。 确定的相关性味道指数被过滤。 只有这样的建议被提供给用户A,其相关联的相关性味道指数满足过滤标准。 因此,提交给用户A的无关建议的数量可以大大减少。