The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Abstract
Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, Hidasi et al.~recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method.
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Cites
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Deep Neural Networks for YouTube Recommendations
Covington, P., Adams, J., & Sargin, E. (2016) · Proceedings of the 10th ACM Conference on Recommender Systems · doi:10.1145/2959100.2959190 · Cited by 3,539
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
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Image-Based Recommendations on Styles and Substitutes
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Collaborative Deep Learning for Recommender Systems
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Improved Recurrent Neural Networks for Session-based Recommendations
Tan, Y. K., Xu, X., & Liu, Y. (2016) · Proceedings of the 1st Workshop on Deep Learning for Recommender Systems · doi:10.1145/2988450.2988452 · Cited by 779
Convolutional Matrix Factorization for Document Context-Aware Recommendation
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Cited by
Session-Based Recommendation with Graph Neural Networks
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301346 · Cited by 1,532
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
Self-citationNews recommender systems – Survey and roads ahead
Karimi, M., Jannach, D., & Jugovac, M. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.04.008 · Cited by 336
Self-citationAlgorithmic and Ethical Aspects of Recommender Systems in e-Commerce
Paraschakis, D. (2018) · doi:10.24834/2043/24268 · Cited by 35
Metadata
- Authors
- Jannach, D., & Ludewig, M.
- Venue
- Proceedings of the Eleventh ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2017-08-27
- Pages
- 306-310
- OpenAlex
- W2746011824
- Cited by
- 490
- References
- 33
- FWCI
- 33.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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