NDLI: Exploiting homophily effect for trust prediction

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Exploiting homophily effect for trust prediction

Content Provider	ACM Digital Library
Author	Gao, Huiji Hu, Xia Tang, Jiliang Liu, Huan
Abstract	Trust plays a crucial role for online users who seek reliable information. However, in reality, user-specified trust relations are very sparse, i.e., a tiny number of pairs of users with trust relations are buried in a disproportionately large number of pairs without trust relations, making trust prediction a daunting task. As an important social concept, however, trust has received growing attention and interest. Social theories are developed for understanding trust. Homophily is one of the most important theories that explain why trust relations are established. Exploiting the homophily effect for trust prediction provides challenges and opportunities. In this paper, we embark on the challenges to investigate the trust prediction problem with the homophily effect. First, we delineate how it differs from existing approaches to trust prediction in an unsupervised setting. Next, we formulate the new trust prediction problem into an optimization problem integrated with homophily, empirically evaluate our approach on two datasets from real-world product review sites, and compare with representative algorithms to gain a deep understanding of the role of homophily in trust prediction.
Starting Page	53
Ending Page	62
Page Count	10
File Format	PDF
ISBN	9781450318693
DOI	10.1145/2433396.2433405
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-02-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trust network Trust prediction Homophily regularization Social correlation Homophily effect
Content Type	Text
Resource Type	Article

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