Abstract: The candidate matching, i.e., retrieving potentially relevant candidate items to users for further ranking, is highly important since even the cleverest housewife cannot bake bread without flour. While many research efforts are devoted to the ranking stage, few works focus on the former one where the space for more sophisticated models seems to be limited due to the requirement of low computation cost, especially for an industrial system. No matter which scenario, the matching stage is highly important since even the cleverest housewife cannot bake bread without flour. In this talk, we will introduce the background knowledge towards this task at first. Then, an overview on the recent efforts of this line will be presented. At last, we will discuss the possible directions in the near future.
Speaker Bio: Dr. Chenliang Li is a Professor at School of Cyber Science and Engineering, Wuhan University. His research interests inlcude information retrieval, natural language processing and social media analysis. He has published over 90 research papers on leading academic conferences and journals such as SIGIR, ACL, WWW, IJCAI, AAAI, TKDE and TOIS. He has served as Associate Editor / Editorial Board Member for ACM TOIS, ACM TALLIP, IPM and JASIST. His research won the SIGIR 2016 Best Student Paper Honorable Mention and TKDE Featured Spotlight Paper.
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