S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks

TL;DR

S-Walk is a scalable, efficient session-based recommendation method that leverages random walks to capture both intra- and inter-session item relationships, achieving high accuracy and fast inference.

Contribution

It introduces a novel random walk-based approach for SR that models high-order item relationships efficiently using linear algebra, enabling scalability and compression.

Findings

Achieves state-of-the-art or comparable accuracy on four datasets.

Provides two or more orders of magnitude faster inference than DNN models.

Models high-order relationships with closed-form solutions for efficiency.

Abstract

Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra- and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.