Joint Collaborative Ranking with Social Relationships in Top-N Recommendation

Abstract

With the advent of learning to rank methods, relevant studies showed that Collaborative Ranking (CR) models can produce accurate ranked lists in the top-N recommendation problem. However, in practice several real-world problems decrease their ranking performance, such as the sparsity and cold-start problems, which often occur in recommendation systems for inactive or new users. In this study, to account for the fact that the selections of social friends can improve the recommendation accuracy, we propose a joint CR model based on the users' social relationships. We propose two different CR strategies based on the notions of Social Reverse Height and Social Height, which consider how well the relevant and irrelevant items of users and their social friends have been ranked at the top of the list, respectively. We focus on the top of the list mainly because users see the top-N recommendations in real-world applications, and not the whole ranked list. Furthermore, we formulate a joint objective function to consider both CR strategies, and propose an alternating minimization algorithm to learn our joint CR model. Our experiments on benchmark datasets show that our proposed joint CR model outperforms other state-of-the-art models that either consider social relationships or focus on the ranking performance at the top of the list.