  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.
