Learning to Rank for Information Retrieval and Its Related Applications
Information retrieval has made significant progress in retrieving relevant results for user information need. How to retrieve relevant information more accurately has been one of the important topics in the communities of information retrieval and machine learning. In this talk, I will briefly describe the problem of learning to rank, and share my experience of how to design a learning-based ranking algorithm for the problem. In addition, I will also introduce some related applications to which the learning to rank techniques can be applied, including machine translation, object search, entity search, and recommender system. Furthermore, I will show some emerging search engines in the context of social networking environment.
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.