Next-item Recommendations in Short Sessions

Wenzhuo Song

Abstract:

The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to
model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on
long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most
of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited
contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the
success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation
as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective
SBRS called INter-SEssion collaborative Recommender neTwork (INSERT) for next-item recommendations in short sessions. With the
carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a
given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions
similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained
similar sessions are then utilized to complement and optimize the preference representation learned from the current short session
by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two
real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item
recommendations in short sessions.