Collaborative Ranking via Learning Social Experts - Zhi Yin; Xin Wang; Xiaoqiong Wu; Chen Liang; Congfu Xu (Proceedings - International Conference on Tools with Artificial Intelligence, TAI, 2014)
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.