TY - JOUR
T1 - Jointly modeling intra- and inter-session dependencies with graph neural networks for session-based recommendations
AU - Wang, Jingjing
AU - Xie, Haoran
AU - Wang, Fu Lee
AU - Lee, Lap Kei
AU - Wei, Mingqiang
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2023/3
Y1 - 2023/3
N2 - Recently, graph neural networks (GNNs) have achieved promising results in session-based recommendation. Existing methods typically construct a local session graph and a global session graph to explore complex item transition patterns. However, studies have seldom investigated the repeat consumption phenomenon in a local graph. In addition, it is challenging to retrieve relevant adjacent nodes from the whole training set owing to computational complexity and space constraints. In this study, we use a GNN to jointly model intra- and inter-session item dependencies for session-based recommendations. We construct a repeat-aware local session graph to encode the intra-item dependencies and generate the session representation with positional awareness. Then, we use sessions from the current mini-batch instead of the whole training set to construct a global graph, which we refer to as the session-level global graph. Next, we aggregate the K-nearest neighbors to generate the final session representation, which enables easy and efficient neighbor searching. Extensive experiments on three real-world recommendation datasets demonstrate that RN-GNN outperforms state-of-the-art methods.
AB - Recently, graph neural networks (GNNs) have achieved promising results in session-based recommendation. Existing methods typically construct a local session graph and a global session graph to explore complex item transition patterns. However, studies have seldom investigated the repeat consumption phenomenon in a local graph. In addition, it is challenging to retrieve relevant adjacent nodes from the whole training set owing to computational complexity and space constraints. In this study, we use a GNN to jointly model intra- and inter-session item dependencies for session-based recommendations. We construct a repeat-aware local session graph to encode the intra-item dependencies and generate the session representation with positional awareness. Then, we use sessions from the current mini-batch instead of the whole training set to construct a global graph, which we refer to as the session-level global graph. Next, we aggregate the K-nearest neighbors to generate the final session representation, which enables easy and efficient neighbor searching. Extensive experiments on three real-world recommendation datasets demonstrate that RN-GNN outperforms state-of-the-art methods.
KW - Graph attention neural networks
KW - Reverse-position
KW - Session-based recommendation
UR - http://www.scopus.com/inward/record.url?scp=85144033601&partnerID=8YFLogxK
U2 - 10.1016/j.ipm.2022.103209
DO - 10.1016/j.ipm.2022.103209
M3 - Article
AN - SCOPUS:85144033601
SN - 0306-4573
VL - 60
JO - Information Processing and Management
JF - Information Processing and Management
IS - 2
M1 - 103209
ER -