On Recommendations via Deep and Broad Learning
Keynote Speech II
Department of Computer Science
University of Illinois at Chicago
Chicago, IL 60607
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. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. 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 IEEE Intl. Conference on Data Mining (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 144,700 times with an H-index of 175. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).
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.