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User-based Collaborative Filtering (CF) is one of the most popular approachesto create recommender systems. This approach is based on finding the mostrelevant k users from whose rating history we can extract items to recommend.CF, however, suffers from data sparsity and the cold-start problem since usersoften rate only a small fraction of available items. One solution is toincorporate additional information into the recommendation process such asexplicit trust scores that are assigned by users to others or implicit trustrelationships that result from social connections between users. Suchrelationships typically form a very sparse trust network, which can be utilizedto generate recommendations for users based on people they trust. In our work,we explore the use of a measure from network science, i.e. regular equivalence,applied to a trust network to generate a similarity matrix that is used toselect the k-nearest neighbors for recommending items. We evaluate our approachon Epinions and we find that we can outperform related methods for tacklingcold-start users in terms of recommendation accuracy.

Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence