In recent years, sequential/session-based recommendations have emerged as a new recommendation paradigm to well model users’ dynamic and short-term preferences for more accurate and timely recommendations. They have been employed in various application domains, including e-Commerce, video platforms/apps, news websites/apps, etc. and have achieved great success. In this talk, I will briefly share some of our work in this vibrant research area. First, I will introduce the background, and research problem of sequential/session-based recommendation. Then, I will introduce three specific sub-areas in this area (i.e., next-product recommendation, next-basket recommendation, next-news recommendation) via sharing some of our specific work in each of them, followed by an overview of some applications of sequential/session-based recommendations. Finally, I will conclude this talk by sharing some future directions.
Speaker Bio
Shoujin Wang is a Lecturer in Data Science at University of Technology Sydney. Shoujin obtained his PhD in data science from University of Technology Sydney in 2019. His main research interests include data mining, machine learning, recommender systems and fake news mitigation. He has published more than 50 research papers in these areas, most of which were published at premier data science and AI conferences or journals, like The WebConf, SIGIR, AAAI, IJCAI, TKDE and ACM CSUR. Shoujin has generally served as a PC member or a senior PC member at over 10 premier international data science conferences including KDD, AAAI, IJCAI, WSDM, CIKM and a reviewer for more than 10 prestigious journals including Machine Learning, IEEE TKDE, ACM TOIS, etc. Shoujin is the recipient of 2021 DAAD AINet Fellowship, 2022 Club Melbourne Fellowship and 2022 DSAA Next-generation Data Scientist Award.
More Details
- When: Wed 17 May 2023, at 1:00 pm (GMT+10)
- Speaker: Dr Shoujin Wang (University of Technology Sydney)
- Host: Dr Rocky Chen
- Zoom: https://uqz.zoom.us/j/82896549343
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