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[卓越演講]On Recommendations via Deep and Broad Learning

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[卓越演講]On Recommendations via Deep and Broad Learning

  • 講者俞士綸 特聘教授 (Department of Computer Science, University of Illinois at Chicago, USA)
    邀請人:楊得年
  • 時間2023-08-16 (Wed.) 10:00 – 12:00
  • 地點資訊所N106演講廳
線上串流
會議鏈結:【webex
會議號:2516 643 8099
密碼:Bmab9pXMd45
摘要
As the variety of products and services increases, recommender systems play a critical role in helping customers by presenting products or services that are likely of interests to them. In the era of big data, there are abundant of data available across many different data sources in various modalities. In additional to users rating information on products, other relevant information sources can include social network, knowledge base, product description and reviews as well as context and temporal information. Even cross domain and cross site information can be useful. Here we focus on applying broad learning to fuse multiple information sources of diverse varieties together and carrying out synergistic deep recommendation task across these fused sources in a unified way. In this talk, we examine the various heterogeneous information sources, and ways on applying deep and broad learning to improve effectiveness on recommendation systems.
BIO
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from ICDM in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,300 referred conference and journal papers cited more than 166,700 times with an H-index of 183. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM TKDD (2011-2017) and IEEE TKDE (2001-2004).