Collaborative Ranking with Social Relationships for Top-N Recommendations

Dimitrios Rafailidis, Fabio Crestani

Abstract: Recommendation systems have gained a lot of attention because of their importance for handling the unprecedentedly large amount of available content on the Web, such as movies, music, books, etc. Although Collaborative Ranking (CR) models can produce accurate recommendation lists, in practice several real-world problems decrease their ranking performance, such as the sparsity and cold start problems. Here, to account for the fact that the selections of social friends can leverage the recommendation accuracy, we propose SCR, a Social CR model. Our model learns personalized ranking functions collaboratively, using the notion of Social Reverse Height, that is, considering how well the relevant items of users and their social friends have been ranked at the top of the list. The reason that we focus on the top of the list is that users mainly see the top-N recommendations, and not the whole ranked list. In our experiments with a benchmark data set from Epinions, we show that our SCR model performs better than state-of-the-art CR models that either consider social relationships or focus on the ranking performance at the top of the list.