Abstract
Session-based recommendation, which aims to predict the user’s immediate next action based on
anonymous sessions, is a key task in many online
services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved
significant success in various sequence modeling
tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences. In this paper, we propose a graph contextualized self-attention model
(GC-SAN), which utilizes both graph neural network and self-attention mechanism, for sessionbased recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN). Then each session learns long-range dependencies by applying
the self-attention mechanism. Finally, each session
is represented as a linear combination of the global
preference and the current interest of that session.
Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art
methods consistently