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 its unique characteristics, i.e., topical coherence and sequential dependency over items within the session, repeated item consumption, and timeliness of sessions. In this paper, we propose simple-yet-effective session-aware linear models, considering the holistic aspects of the sessions. This comprehensive nature of our models helps improve the quality of recommendations. More importantly, it provides a generalized framework for various types of session data. Because our models can be solved by a closed-form solution, they are highly scalable. Experimental results demonstrate that our simple linear models show competitive or state-of-the-art performance in various metrics on multiple real-world datasets.

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