Sequential dependency enhanced graph neural networks for session-based recommendations

Wei Guo, Shoujin Wang, Wenpeng Lu, Hao Wu, Qian Zhang, Zhufeng Shao

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

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 dependencies 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.
Original languageEnglish
Title of host publication2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-10
Number of pages10
ISBN (Print)9781665420990
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021 - Porto, Portugal
Duration: 6 Oct 20219 Oct 2021

Conference

Conference8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021
CountryPortugal
CityPorto
Period6/10/219/10/21

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