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From Ratings to Trust: an Empirical Study of Implicit Trust in Recommender Systems

Guibing Guo, Jie Zhang, Daniel Thalmann, Anirban Basu and Neils Yorke-Smith

In: The 29th ACM Symposium on Applied Computing (SAC) RS track, Gyeongju, Korea.

Year: 2014.

Abstract: Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and future metrics should be designed more carefully..

Fulltext: PDF.