Second-order Mining for Active Collaborative Filtering

Abstract

Abstract Active learning for collaborative filtering tasks draws many attentions from the research community. It can capture the user's interest with greatly reduced labeling burden for the online user. High quality recommendation can thus be made with good user experience. In this paper we address the efficiency challenge of current active learning methods for online and interactive applications by using the second-order mining techniques. According to the global latent semantic model learnt from the feedbacks of historical users to items, we propose an intuitive and efficient query strategy for the item selection for new active user. The time complexity in each query is reduced greatly to constant O (1). Experimental results on the public available data sets show the efficiency and effectiveness of our method.