时间：1月3日星期五上午10：00
地点：浙大紫金港校区CAD&CG国家重点实验室402室
题目：Inferring User’s Preferences from Crowdsourced Pairwise Comparisons:A Matrix Completion Approach
报告人: Prof. Rong Jin
主持人：蔡登  教授

Abstract：Inferring user preferences of items is an important problem and has found numerous applications. This work focuses on the scenario where the explicit feature representation of items is unavailable, a setup that is similar to collaborative filtering.In order to learn a user’s preference from his/her response to only a small number of pairwise comparisons questions,we propose to leverage the pairwise comparisons made by many crowd users, a problem to which we refer to as crowdrank.The proposed crowdranking framework is based on the theory of matrix completion, and we present efficient algorithms for solving the related optimization problem. Our theoretical analysis shows that, on average, only O(r log m)pairwise queries are needed to accurately recover the ranking list of m items for the target user, where r is the rank of the unknown rating matrix. Our empirical study with two real-world benchmark data sets for collaborative filtering and one crowdranking data collected by us via Amazon Mechanical Turk shows the promising performance of the proposed algorithm compared to the state-of-the-art approaches.
Bio: Dr. Rong Jin is a full Professor of the Computer and Science Engineering Dept. at Michigan State University. He is working on statistical machine learning for big data analysis. In the past, Dr. Jin has worked on various machine learning algorithms and theories. He also has extensive experience with applying machine learning techniques to large-scale information management problems. He has published over 200 research articles on the related topics. Dr. Jin holds a B.A. in Engineering from Tianjin University, an M.S. in Physics from Beijing University, and an M.S. and Ph.D. in Computer Science from Carnegie Mellon University. He received the NSF career award in 2006 and best paper award from COLT in 2012.