The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Open accessAbstract
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and [email protected] over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
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
Deep Interest Evolution Network for Click-Through Rate Prediction
Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., Zhu, X., & Gai, K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015941 · Cited by 1,099
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Qiu, R., Li, J., Huang, Z., & YIn, H. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3358010 · Cited by 339
Evaluation of session-based recommendation algorithms
Ludewig, M., & Jannach, D. (2018) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-018-9209-6 · Cited by 336
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
- Hidasi, B., & Karatzoglou, A.
- Venue
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2018-10-17
- Pages
- 843-852
- OpenAlex
- W2626454364
- Cited by
- 925
- References
- 3
- FWCI
- 61.59
- Type
- conference paper
- Language
- en
- Open access
- green
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- Hidasi, B., & Karatzoglou, A. (2018). Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. https://doi.org/10.1145/3269206.3271761
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