Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
4-2019
Abstract
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.
Keywords
Recommender Systems, Preference Learning, Sequential Recommendation, Basket-Sensitive Recommendation, Item-Item Association, Sequential Association, Correlative Association, Basket-Oriented Association
Degree Awarded
PhD in Information Systems
Discipline
Databases and Information Systems | Data Storage Systems
Supervisor(s)
LAUW, Hady Wirawan
Publisher
Singapore Management University
City or Country
Singapore
Citation
LE DUC TRONG, Duc-Trong.
Modeling sequential and basket-oriented associations for top-K recommendation. (2019).
Available at: https://ink.library.smu.edu.sg/etd_coll/198
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.