Popularity tendency analysis of ranking-oriented collaborative filtering from the perspective of loss function

Collaborative filtering (CF) has been the most popular approach for recommender systems in recent years. In order to analyze the property of a ranking-oriented CF algorithm directly and be able to improve its performance, this paper investigates the ranking-oriented CF from the perspective of loss function. To gain the insight into the popular bias problem, we also study the tendency of a CF algorithm in recommending the most popular items, and show that such popularity tendency can be adjusted through setting different parameters in our models. After analyzing two state-of-the-art algorithms, we propose in this paper two models using the generalized logistic loss function and the hinge loss function, respectively. The experimental results show that the proposed methods outperform the state-of-the-art algorithms on two real data sets.