Title: Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence,
Abstract: User-based Collaborative Filtering (CF) is one of the most popular approaches
to create recommender systems. This approach is based on finding the most
relevant k users from whose rating history we can extract items to recommend.
CF, however, suffers from data sparsity and the cold-start problem since users
often rate only a small fraction of available items. One solution is to
incorporate additional information into the recommendation process such as
explicit trust scores that are assigned by users to others or implicit trust
relationships that result from social connections between users. Such
relationships typically form a very sparse trust network, which can be utilized
to 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 to
select the k-nearest neighbors for recommending items. We evaluate our approach
on Epinions and we find that we can outperform related methods for tackling
cold-start users in terms of recommendation accuracy.