A biased random walk recommender based on Rejection Sampling

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 unif...

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Bibliographic Details
Published in:	Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining : ASONAM 2013 : Niagara Falls, Canada, August 25-28, 2013 pp. 648 - 652
Main Authors:	Alexandridis, Georgios, Siolas, Georgios, Stafylopatis, Andreas
Format:	Conference Proceeding
Language:	English
Published:	ACM and IEEE 01.08.2013
Subjects:	Collaboration Conferences Correlation Measurement Probability distribution Recommender systems Social network services
Online Access:	Get full text
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Summary:	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.
DOI:	10.1145/2492517.2492653