TY - GEN
T1 - Sequential Dependency Enhanced Graph Neural Networks for Session-based Recommendations
AU - Guo, Wei
AU - Wang, Shoujin
AU - Lu, Wenpeng
AU - Wu, Hao
AU - Zhang, Qian
AU - Shao, Zhufeng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-.based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential aependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.
AB - Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-.based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential aependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.
KW - Graph Neural Networks
KW - Sequential Dependencies
KW - Session-based Recommendations
KW - Transitions of Items
UR - http://www.scopus.com/inward/record.url?scp=85124518572&partnerID=8YFLogxK
U2 - 10.1109/DSAA53316.2021.9564224
DO - 10.1109/DSAA53316.2021.9564224
M3 - Conference contribution
AN - SCOPUS:85124518572
T3 - 2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021
BT - 2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021
Y2 - 6 October 2021 through 9 October 2021
ER -