Top-N Sequential Recommendation and Top-k Group Nearest Neighbor Search
Wang Ke

Speaker Event details

Professor, School of Computing Science
Simon Fraser University




9 May 2018, Wednesday

2.30pm – 3.30pm

Seminar Room 3-1, Level 3
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902

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This talk has two parts related to ranked recommendation and query answering. The first part presents our project on top-N sequential recommendation through embedding a sequence of recent items in user behaviors into an “image” in the time and latent spaces and learning sequential patterns as local features of the image using convolutional filters. This embedding enables a richer representation, therefore, better captures sequential signals. The second part presents our project on finding top-k nearest answers (such as meeting locations) for several users as one group, called group nearest neighbor search, such that user location privacy is preserved against other users as well as the service provider.

About the speaker

WANG Ke is currently a professor at School of Computing Science, Simon Fraser University. Ke Wang's research interests include database technology, data mining and knowledge discovery, with emphasis on massive datasets, graph and network data, and data privacy. Ke Wang has published in major venues of database, information retrieval, and data mining, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM, ICDM, WWW, AAAI, and CIKM. He co-authored a book "Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques". He was an associate editor of ACM TKDD, IEEE TKDE, and an editorial board member for Journal of Data Mining and Knowledge Discovery.


This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.

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