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 Modeling Multiple Tasks in Recommendation Systems |  | DO Dinh Hieu PhD Candidate
School of Computing and Information Systems
Singapore Management University
| Research Area Dissertation Committee Research Advisor Committee Members External Member - Jisun AN, Assistant Professor, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, United States
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| | Date 11 July 2025 (Friday) | Time 10:00am - 11:00am | Venue Meeting room 4.4,
Level 4
School of Computing and Information Systems 1,
Singapore Management University,
80 Stamford Road
Singapore 178902 | Please register by 9 July 2025. We look forward to seeing you at this research seminar. 
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| | ABOUT THE TALK Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.
However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are constantly emerging, user preferences shift over time, and interactions arrive as a continuous stream. Moreover, data is often fragmented across multiple platforms or domains, each with its own characteristics and challenges. These factors introduce complexities such as the need for continual adaptation and transferring knowledge across domains.
Recognizing these limitations, this dissertation aims to bridge the gap by formulating recommendation problems that better reflect real-world scenarios. The primary goal is to design dynamic frameworks that efficiently learn from new data streams, handle evolving user behaviors and item catalogs, and address data fragmentation across platforms by enabling knowledge transfer between tasks and domains. | | | SPEAKER BIOGRAPHY Do Dinh Hieu is currently a PhD candidate at SMU School of Computing and Information Systems, advised by Prof. Hady W. Lauw. He holds a B.Sc. in Computer Science from Vietnam National University, Hanoi. His research focuses on the formulation and solution of dynamic, real-world recommender systems. Driven by curiosity, he enjoys exploring new and interesting knowledge both within and beyond his field. |
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