讲座信息:Recent Advance on Recommendation Methods for Implicit Feedback

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讲座信息:Recent Advance on Recommendation Methods for Implicit Feedback

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时间: 14:00, Apr. 18, 2017
地点:信息楼417
题目: Recent Advance on Recommendation Methods for Implicit Feedback
演讲者: 何向南 National University of Singapore
Abstract: In recent years, the focus of recommender system research has shifted from explicit feedback problems such as rating prediction to implicit feedback problems. In this talk, I will introduce our recent two works for addressing the recommendation problem with implicit feedback.
In our first work [He et al. WWW 2017], we develop techniques based on neural networks for recommendation.
In our second work [Bayer and He et al. WWW 2017], we propose a novel and generic optimization method on par with BPR (Bayesian Personalized Ranking) for learning recommender models from implicit feedback. This work provides the theory and building blocks to derive efficient implicit CD algorithms for (multi-)linear recommender models.
Short Bio: Dr. Xiangnan He is currently a postdoctoral research fellow with the Lab for Media Search, National University of Singapore. His research interests span recommender system, information retrieval, multi-media and natural language processing. His works have appeared in several top-tier conferences such as SIGIR, WWW, MM, CIKM and AAAI, and top-tier journals including TKDE and TOIS. His work on recommender system has received the Best Paper Award Honorable Mention of ACM SIGIR 2016. Moreover, he has served as the PC member for the prestigious conferences including SIGIR, WWW, MM, CIKM and EMNLP, and invited reviewer for prestigious journals including TKDE, WWWJ and TIIS.