PA-GGAN: Session-Based Recommendation with Position-Aware Gated Graph Attention Network

Abstract

Session-based recommendation aims to predict user behaviors based on anonymous sessions. Recently, session sequences are modeled as graph-structured data. Based on the session graphs, Graph Neural Networks (GNNs) can capture complex transitions of items, compared with previous conventional sequential methods. However, the existing graph-construction approaches have limited power in capturing the position information of items in the session sequences. In addition, GNNs employed in the existing session-based recommendation are not capable to attend over their neighborhoods' features in feature aggregation phase. In this paper, we propose a Position-Aware Gated Graph Attention Network (PAGGAN). Specifically, a reverse-position mechanism is proposed to assign position embeddings to nodes in the session graphs based on the order of items in each session sequence. And we enhance Gated Graph Neural Network (GGNN) by introducing self-attention mechanism when aggregating features from nodes. Experimental results on two real-world datasets show that the PA-GGAN outperforms state-of-the-art methods.