Collaborative Ranking via Learning Social Experts

Recommendation as a universal service has driven much research works, among which explicit feedback estimation (e.g., Rating prediction in the Netflix competition) is probably the most well-known and well-studied problem. However, in various online and mobile applications, data resources of implicit feedbacks from users´ interaction behaviors and linked connections from pervasive social media sites are more abundant. In this paper, we aim to integrate the users´ implicit feedbacks and social connections in order to improve the ranking-oriented recommendation performance. One fundamental challenge is the noise of the social connections, which may cause incorrect social influences during learning of users´ preferences. As a response, we propose to learn social experts (rather than to rely on connected individual users) as the major influence source for a certain user, which is likely to generate more accurate social influences. Specifically, we design a novel user preference generation function so as to seamlessly incorporate influences from the learned social experts. We then develop a general learning algorithm correspondingly, i.e., Collaborative ranking via learning social experts (CRSE). To verify our idea of learning social experts, we study the ranking performance of CRSE on two real-world datasets, and find that it can produce more accurate recommendations than the state-of-the-art methods.