PhD Dissertation Defense by LE Duc Trong | Modeling Sequential and Basket-Oriented Associations for Top-K Recommendation

About The Talk

Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially with the notions of sequence-aware and basket-level adoptions . Sequences are created by sorting item adoptions chronologically. The associations between items along sequences, referred to as “sequential associations”, indicate the influence of the preceding adoptions on the following adoptions. Considering a basket of items consumed at the same time step (e.g., a session, a day), “basket-oriented associations” imply correlative dependencies among these items. In this dissertation, we present research works on modeling “sequential & basket-oriented associations” independently and jointly for the Top-K recommendation task.

Speaker Biography

LE Duc Trong is a PhD candidate at Singapore Management University (SMU), advised by Associate Professor Hady W. LAUW and Assistant Professor FANG Yuan. He received his bachelor degree in Information Technology from University of Engineering and Technology, Vietnam National University, Hanoi. At SMU, his research topic is recommender systems, which exploit the dependency among items, e.g., sequential & correlative dependencies, to improve top-K recommendation performance.