Implicit Session Contexts for Next-Item Recommendations
- URL: http://arxiv.org/abs/2208.09076v1
- Date: Thu, 18 Aug 2022 21:48:16 GMT
- Title: Implicit Session Contexts for Next-Item Recommendations
- Authors: Sejoon Oh, Ankur Bhardwaj, Jongseok Han, Sungchul Kim, Ryan A. Rossi,
and Srijan Kumar
- Abstract summary: Session contexts are not explicitly given in most datasets.
We propose ISCON, which implicitly contextualizes sessions.
ISCON has superior next-item prediction accuracy than state-of-the-art models.
- Score: 27.250637187941532
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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