- Session-aware Linear Item-Item Models for Session-based Recommendation (심현정 교수님 연구실)
- 게시글 내용
Session-aware Linear Item-Item Models for Session-based Recommendation
The Web Conference (WWW), 2021. (한국연구재단 인정 CS분야 최우수 국제학술대회 - IF 4인정)
M Choi, J Kim, J Lee, H Shim and J Lee
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.