  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.
