  Session-based recommendation aims to predict user's next behavior from
current session and previous anonymous sessions. Capturing long-range
dependencies between items is a vital challenge in session-based
recommendation. A novel approach is proposed for session-based recommendation
with self-attention networks (SR-SAN) as a remedy. The self-attention networks
(SAN) allow SR-SAN capture the global dependencies among all items of a session
regardless of their distance. In SR-SAN, a single item latent vector is used to
capture both current interest and global interest instead of session embedding
which is composed of current interest embedding and global interest embedding.
Some experiments have been performed on some open benchmark datasets.
Experimental results show that the proposed method outperforms some
state-of-the-arts by comparisons.
