  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.
