Abstract
Session-based recommendations (SBRs) recommend the next item for an anonymous user by modeling the dependencies between items in a session. Benefiting from the superiority of graph neural networks (GNN) in learning complex dependencies, GNN-based SBRs have become the main stream of SBRs in recent years. Most GNN-based SBRs are based on a strong assumption of adjacent dependency, which means any two adjacent items in a session are necessarily dependent here. However, based on our observation, the adjacency does not necessarily indicate dependency due to the uncertainty and complexity of user behaviours. Therefore, the aforementioned assumption does not always hold in the real-world cases and thus easily leads to two deficiencies: (1) the introduction of false dependencies between items which are adjacent in a session but are not really dependent, and (2) the missing of true dependencies between items which are not adjacent but are actually dependent. Such deficiencies significantly downgrade accurate dependency learning and thus reduce the recommendation performance. Aiming to address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes the topic information extracted from items’ reviews to refine dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms the state-of-the-art methods (The implementation is available at https://github.com/Nishikata97/RI-GNN.).
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Acknowledgment
Wenpeng Lu is the corresponding author. The research work is partly supported by National Natural Science Foundation of China under Grant No. 11901325 and No. 61502259, National Key R&D Program of China under Grant No. 2018YFC0831700, Key Program of Science and Technology of Shandong Province under Grant No. 2020CXGC010901 and No. 2019JZZY020124, and Natural Science Foundation of Shandong Province under Grant ZR2021MF079.
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Zhang, Q., Wang, S., Lu, W., Feng, C., Peng, X., Wang, Q. (2022). Rethinking Adjacent Dependency in Session-Based Recommendations. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_24
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DOI: https://doi.org/10.1007/978-3-031-05981-0_24
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