Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.

中文翻译：

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多方面的基于信任的协同过滤

许多协作推荐系统利用社会相关理论来提高建议性能。然而，他们关注用户之间的明确关系，而忽略了有助于确定用户全球声誉的其他类型的信息；例如，公众对审稿人质量的认可。我们有兴趣了解这些额外类型的反馈是否以及何时改进 Top-N 推荐。为此，我们提出了一个多方面的信任模型，以整合以社交链接为代表的本地信任与社交网络提供的各种类型的全局信任证据。我们的目标是识别数据的一般类别，以使我们的模型适用于不同的案例研究。然后，我们通过将模型应用于用户到用户协作过滤 (U2UCF) 的变体来测试该模型，该变体支持评级相似性、源自社会关系的本地信任以及评级预测的多方面声誉的融合。我们在两个数据集上测试我们的模型：Yelp 一个发布用户之间的通用好友关系，但提供不同类型的信任反馈，包括用户个人资料认可。LibraryThing 数据集提供的反馈类型较少，但它提供了更多选择性的朋友关系，旨在实现内容共享。我们的实验结果表明，在 Yelp 数据集上，我们的模型优于 U2UCF 和仅使用评分相似性和社会关系的最先进的基于信任的推荐器。不同的是，在 LibraryThing 数据集中，社会关系和评分相似度的结合达到了最好的效果。我们学到的教训是，多方面的信任可以成为一种有价值的推荐信息。但是，在应用程序域中使用它之前，必须对可用信任证据的类型和数量进行分析，以评估其对推荐性能的实际影响。