From ratings to trust

An empirical study of implicit trust in recommender systems

Conference paper (2014)

Authors

Guibing Guo Nanyang Technological University
Jie Zhang Nanyang Technological University
Daniel Thalmann Nanyang Technological University
Anirban Basu KDDI R and D Laboratories, Inc.
N. Yorke-Smith American University of Beirut, University of Cambridge
Affiliation
External organisation
Similarity Recommender systems Trust metrics Ratings
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Published Date

2014

Language

English

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Affiliation

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Abstract

Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the 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 indicate that future metrics should be designed more carefully.