Abstract
Session-based recommendation (SBR) is a challenging task that involves predicting a user’s next item click based on their recent session history. Presently, many state-of-the-art methodologies employ graph neural networks to model item transitions. Notwithstanding their impressive performance, graph-based models encounter significant challenges when confronted with intricate session dependencies and data sparsity in real-world scenarios, ultimately constraining their capacity to enhance recommendation accuracy. In recognition of these challenges, we introduce an innovative methodology known as ‘Mssen,’ which stands for Multi-collaborative self-supervised learning in hypergraph neural networks. Mssen is meticulously crafted to adeptly discern user intent. Our approach initiates by representing session-based data as a hypergraph, adeptly capturing intricate, high-order relationships. Subsequently, we employ self-supervised learning on item-session hypergraphs to mitigate the challenges of data sparsity, all without necessitating manual fine-tuning, extensive search, or domain-specific expertise in augmentation selection. Comprehensive experimental analyses conducted across multiple datasets consistently underscore the superior performance of our approach when compared to existing methodologies.- Anthology ID:
- 2024.lrec-main.745
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 8493–8504
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.745
- DOI:
- Cite (ACL):
- Xiangping Zheng, Bo Wu, Alex X. Zhang, and Wei Li. 2024. Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8493–8504, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (Zheng et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.745.pdf