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Session-aware Linear Item-Item Models for Session-based Recommendation

Authors :
Choi, Minjin
Kim, jinhong
Lee, Joonseok
Shim, Hyunjung
Lee, Jongwuk
Publication Year :
2021

Abstract

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.<br />Comment: In Proceedings of the Web Conference 2021. 12 pages

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2103.16104
Document Type :
Working Paper
Full Text :
https://doi.org/10.1145/3442381.3450005