Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
A recommender system based on local random walks and spectral methods
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this paper, we focus on Recommender Systems that are enhanced with social information in the form of trust statements between their users. The trust information may be processed in a number of ways, including the random walks in the Social Graph, where every step in the walk is chosen almost uniformly at random from the available choices. Even though this strategy yields satisfactory results, it still does not fully exploit the similarity information among users and items. Our work tries to model user-to-user and user-to-item relation as a probability distribution using a novel approach based on Rejection Sampling in order to decide on its next step (biased random walk). Some initial results on reference datasets reveal the potential of this idea.