Session-aware Linear Item-Item Models for Session-based Recommendation

TL;DR

This paper introduces simple linear models for session-based recommendation that consider session characteristics, offering a scalable and effective framework with competitive performance on real-world datasets.

Contribution

The paper presents a generalized linear modeling framework for session-based recommendation that captures session characteristics and is solvable via closed-form solutions.

Findings

Achieves state-of-the-art performance on multiple datasets

Models are highly scalable due to closed-form solutions

Effectively captures session characteristics like sequential dependency

Abstract

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.