Modeling sequential and basket-oriented associations for top-K recommendation

• Main Author: LE DUC TRONG, Duc-Trong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Recommender Systems; Preference Learning; Sequential Recommendation; Basket-Sensitive Recommendation; Item-Item Association; Sequential Association; Correlative Association; Basket-Oriented Association; Databases and Information Systems; Data Storage Systems
• Online Access: https://ink.library.smu.edu.sg/etd_coll/198; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1198&context=etd_coll
• Institution: Singapore Management University

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.