Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth
2018
Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence
RecSys 2018 ACM Vancouver, Canada
User-based Collaborative Filtering (CF) is one of the most popularapproaches to create recommender systems. is approach is basedon nding the most relevant k users from whose rating history wecan extract items to recommend. CF, however, suers from datasparsity and the cold-start problem since users oen rate only asmall fraction of available items. One solution is to incorporateadditional information into the recommendation process such asexplicit trust scores that are assigned by users to others or implicittrust relationships that result from social connections betweenusers. Such relationships typically form a very sparse trust network,which can be utilized to generate recommendations for users basedon people they trust. In our work, we explore the use of a measurefrom network science, i.e. regular equivalence, applied to a trustnetwork to generate a similarity matrix that is used to select thek-nearest neighbors for recommending items. We evaluate ourapproach on Epinions and we nd that we can outperform relatedmethods for tackling cold-start users in terms of recommendationaccuracy