Structural trust inference for social recommendation

The emergence of trust as a key link between users in social networks has been shown to provide an effective means of enhancing the personalization of online user content. However, the availability of such trust information remains a challenge to the algorithms that use it, as the majority of social networks do not provide a means of explicit trust feedback. The first part of this master thesis presents an investigation into the inference of trust relations between actor pairs of a social network, based solely on the structural information of the bipartite graph typical of many on-line social networks. Using intuition inspired from real life observations, this work argues that the popularity of an item in a social graph is inversely related to the level of trust between actor pairs who have rated it. From an existing bipartite social graph, this method computes a new social trust graph, linking actors together by means of symmetric weighted trust relations. Through a set of experiments performed on a real social network dataset, this method is trained, validated and compared to a naive structural trust inference approach producing statistically significant results, and showing strong trust prediction accuracy. Further to this, the use of trust in recommender systems has been shown to improve the accuracy of rating predictions, especially in the case of “controversial” items, where a user’s rating significantly differs from the average. Many different techniques have been used to incorporate trust into recommender systems, each showing encouraging results. However, the lack of trust information available in public datasets has limited the empirical analysis of these techniques and trust-based recommendation in general, with most analysis only being performed on a single dataset. The second part of this work provides a more complete empirical analysis of trust-based recommendation and a further test of the trust inference formula developed in part one. By making use of the trust inference formula developed in the first part of this work, we are able to apply trust-based recommendation techniques to three separate

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