Institute of Information Science, Academia Sinica

This work proposes a music recommendation approach by using various contextual similarity information based on the framework of Factorization Machine (FM). In this talk, we will introduce the FM framework, the idea of feature similarity, and the incorporation of multiple feature similarities into the FM framework. By integrating different feature similarities, the approach enables users to discover diverse items that they never listened before. In addition, in order to avoid the high computational cost and noise within the large number of similarity features, we also present a grouping FM technique to alleviate the problems. In our experiments, a real-world dataset is used to assess the performance of the proposed method. The dataset is collected from an online blogging website (LiveJournal), which includes user listening history, user profiles, social information, and listened music information. Our experimental results show that, with the multiple feature similarities based on the FM framework, the proposed method improves the recommendation performance significantly. Furthermore, with the proposed grouping technique, the efficiency of the method also gets improved.

Ming-Feng Tsai is currently an Assistant Professor in the Department of Computer Science at National Chengchi University. He received his Ph.D. degree from National Taiwan University in 2009. During his Ph.D. study, he was at Microsoft Research Asia as a visiting student with the Web Search & Mining Group, and was awarded by the research center the “Best Intern of the Year.” After receiving his Ph.D. degree, he worked at National University of Singapore as a Research Fellow, participating in a research project related to machine translation. In 2010, sponsored by National Science Council, he joined University of Illinois at Urbana-Champaign as a postdoctoral visitor, working on a project associated with advanced Web search and mining. His research interests span the area of information retrieval, machine learning, web search and mining, social network analysis, and natural language processing, big data analysis.