LookLike: Similarity-based Trust Prediction in Weighted Sign Networks

Abstract

Trust network is widely considered to be one of the most important aspects of social networks. It has many applications in the field of recommender systems and opinion formation. Few researchers have addressed the problem of trust/distrust prediction and, it has not yet been established whether the similarity measures can do trust prediction. The present paper aims to validate that similar users have related trust relationships. To predict trust relations between two users, the LookLike algorithm was introduced. Then we used the LookLike algorithm results as new features for supervised classifiers to predict the trust/distrust label. We chose a list of similarity measures to examined our claim on four real-world trust network datasets. The results demonstrated that there is a strong correlation between users' similarity and their opinion on trust networks. Due to the tight relation between trust prediction and truth discovery, we believe that our similarity-based algorithm could be a promising solution in their challenging domains.