Personalized Ranking Recommendation via Integrating Multiple Feedbacks

Recently, recommender system has attracted a lot of attentions, which helps users to find items of interest through utilizing the user-item interaction information and/or content information associated with users and items. The interaction information (i.e., feedback) between users and items are widely exploited to build recommendation models. The feedback data in recommender systems usually comes in the form of both explicit feedback (e.g., rating) and implicit feedback (e.g., browsing histories, click logs). Although existing works have begun to utilize either explicit or implicit feedback for better recommendation, they did not make best use of these feedback information together. In this paper, we first study the personalized ranking recommendation problem by integrating multiple feedbacks, i.e., one type of explicit feedback and multiple types of implicit feedbacks. Then we propose a unified and flexible personalized ranking framework MFPR to integrate multiple feedbacks. Moreover, as there are no readily available training data, an explicit feedback based training data generation algorithm is designed to generate item pairs with more accurate partial order consistent with the multiple feedbacks for the proposed ranking model. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model, and the integration of multiple feedbacks making up better complementary information significantly improves recommendation performance.