一种面向个性化项目推荐的多方面信任组合模型
A Compositional Model of Multi-faceted Trust for Personalized Item Recommendation
摘要：
基于信任的推荐系统通过利用用户之间的信任网络提供的附加信息来处理冷启动问题，从而改善了关于协同过滤的评级预测。然而，最近的研究对他们提出了挑战，根据这些研究，人们普遍认为使用有关社会关系的数据侵犯了他们自己的隐私。为了解决这个问题，我们扩展了基于信任的推荐系统与信任的额外证据，基于公共匿名信息，我们使它们可配置的数据，可以在给定的应用领域：1 -我们提出了多方面的信任模型（MTM）定义用户之间的信任组合的方式，可能包括或排除它所包含的信息类型。MTM灵活地将社交链接与通过社交网络中的用户简档和用户贡献接收的公共匿名反馈相结合。2 -我们提出了LOCABAL+，基于MTM，它扩展了LOCABAL基于信任的推荐系统与多方面的信任和基于信任的社会正则化。在两个公开的项目评论数据集上进行的实验表明，在用户覆盖率略有下降的情况下，LOCABAL+在准确性、项目排名和错误最小化方面都优于最先进的基于信任的推荐系统和协同过滤系统，无论是在使用关于信任的完整信息时，还是在忽略社会关系时。因此，MTM与LOCABAL+的组合代表了一个有前途的替代国家的最先进的基于信任的推荐系统。
英文摘要：
Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations as a violation of their own privacy. In order to address this issue, we extend trust-based recommender systems with additional evidence about trust, based on public anonymous information, and we make them configurable with respect to the data that can be used in the given application domain: 1 - We propose the Multi-faceted Trust Model (MTM) to define trust among users in a compositional way, possibly including or excluding the types of information it contains. MTM flexibly integrates social links with public anonymous feedback received by user profiles and user contributions in social networks. 2 - We propose LOCABAL+, based on MTM, which extends the LOCABAL trust-based recommender system with multi-faceted trust and trust-based social regularization. Experiments carried out on two public datasets of item reviews show that, with a minor loss of user coverage, LOCABAL+ outperforms state-of-the art trust-based recommender systems and Collaborative Filtering in accuracy, ranking of items and error minimization both when it uses complete information about trust and when it ignores social relations. The combination of MTM with LOCABAL+ thus represents a promising alternative to state-of-the-art trust-based recommender systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
