Abstract
When a Web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining user ratings with information from the trust network. These are the so-called trust-enhanced recommendation systems. While research on the incorporation of trust for recommendations is thriving, the potential of explicitly stated distrust remains almost unexplored. In this article, we introduce a distrust-enhanced recommendation algorithm which has its roots in Golbeck's trust-based weighted mean. Through experiments on a set of reviews from Epinions.com, we show that our new algorithm outperforms its standard trust-only counterpart with respect to accuracy, thereby demonstrating the positive effect that explicit distrust can have on trust-based recommendations.
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Index Terms
Enhancing the trust-based recommendation process with explicit distrust
Recommendations
Learning to recommend with trust and distrust relationships
RecSys '09: Proceedings of the third ACM conference on Recommender systemsWith the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in the industry, traditional recommender systems suffer from several problems. ...
Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation
With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their ...
An explicit trust and distrust clustering based collaborative filtering recommendation approach
A SVD signs based trust and distrust clustering method is proposed.A trust inference method is proposed to compute indirect trust between users.A trust neighbors mining algorithm is proposed to discover trust users.A sparse rating complement algorithm ...






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