The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Sequential User-based Recurrent Neural Network Recommendations
Abstract
Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.
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Cited by
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3357895 · Cited by 2,558
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
Metadata
- Authors
- Donkers, T., Loepp, B., & Ziegler, J.
- Venue
- Proceedings of the Eleventh ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2017-08-27
- Pages
- 152-160
- OpenAlex
- W2750004028
- Cited by
- 359
- References
- 53
- FWCI
- 23.30
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Donkers, T., Loepp, B., & Ziegler, J. (2017). Sequential User-based Recurrent Neural Network Recommendations. Proceedings of the Eleventh ACM Conference on Recommender Systems. https://doi.org/10.1145/3109859.3109877
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