The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A Simple Convolutional Generative Network for Next Item Recommendation
Open accessAbstract
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback.
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Cites
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BackgroundPersonalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
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Cited by
Graph Contextualized Self-Attention Network for Session-based Recommendation
Xu, C., Zhao, P., Liu, Y., Sheng, V. S., Xu, J., Zhuang, F., Fang, J., & Zhou, X. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/547 · Cited by 615
Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., & He, X. (2023) · Proceedings of the 17th ACM Conference on Recommender Systems · doi:10.1145/3604915.3608857 · Cited by 469
Self-citationRecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Zhao, W. X., Mu, S., Hou, Y., Lin, Z., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., et al. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482016 · Cited by 455
Filter-enhanced MLP is All You Need for Sequential Recommendation
Zhou, K., Yu, H., Zhao, W. X., & Wen, J.-R. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512111 · Cited by 401
Hierarchical Gating Networks for Sequential Recommendation
Ma, C., Kang, P., & Liu, X. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330984 · Cited by 322
Metadata
- Authors
- Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., & He, X.
- Venue
- Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2019-01-30
- Pages
- 582-590
- OpenAlex
- W2902040508
- Cited by
- 640
- References
- 26
- FWCI
- 51.18
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., & He, X. (2019). A Simple Convolutional Generative Network for Next Item Recommendation. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3289600.3290975
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