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

Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex
2018 arXiv   pre-print
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
more » ... sers 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.
arXiv:1807.06839v1 fatcat:kkfhyzcu45cfnmhd7am5me4spu