Implicit Session Contexts for Next-Item Recommendations
Abstract
Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.
Cite
@article{arxiv.2208.09076,
title = {Implicit Session Contexts for Next-Item Recommendations},
author = {Sejoon Oh and Ankur Bhardwaj and Jongseok Han and Sungchul Kim and Ryan A. Rossi and Srijan Kumar},
journal= {arXiv preprint arXiv:2208.09076},
year = {2022}
}
Comments
Accepted for publication at: 31st ACM International Conference on Information and Knowledge Management (CIKM 2022) short paper track. Code and data at: https://github.com/srijankr/iscon