The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Open accessAbstract
Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e-commerce and media stream. Recent research of the session-based recommender system mainly focuses on sequential patterns by utilizing the attention mechanism, which is straightforward for the session's natural sequence sorted by time. However, the user's preference is much more complicated than a solely consecutive time pattern in the transition of item choices. In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system. We formulate the next item recommendation within the session as a graph classification problem. Specifically, we propose a weighted attention graph layer and a Readout function to learn embeddings of items and sessions for the next item recommendation. Extensive experiments have been conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica, and the experimental results show that our model outperforms other state-of-the-art methods.
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Cites
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Neural Collaborative Filtering
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Neural Attentive Session-based Recommendation
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Self-citationAn End-to-End Deep Learning Architecture for Graph Classification
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STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation
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Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
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Cited by
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 341
Metadata
- Authors
- Qiu, R., Li, J., Huang, Z., & YIn, H.
- Venue
- Proceedings of the 28th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2019-11-03
- Pages
- 579-588
- OpenAlex
- W2986515219
- Cited by
- 339
- References
- 15
- FWCI
- 23.83
- Type
- conference paper
- Language
- en
- Open access
- green
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