The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Hybrid Recommender System based on Autoencoders
Open accessAbstract
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing values, and (ii) by incorporating side information. The experiments demonstrate that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/items.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Restricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Cited by
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Jannach, D., & Ludewig, M. (2017) · Proceedings of the Eleventh ACM Conference on Recommender Systems · doi:10.1145/3109859.3109872 · Cited by 490
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
Lee, H., Im, J., Jang, S., Cho, H., & Chung, S. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330859 · Cited by 363
Advances in Collaborative Filtering
Koren, Y., Rendle, S., & Bell, R. (2022) · Recommender Systems Handbook · doi:10.1007/978-1-0716-2197-4_3 · Cited by 324
Metadata
- Authors
- Strub, F., Gaudel, R., & Mary, J.
- Venue
- Proceedings of the 1st Workshop on Deep Learning for Recommender Systems
- Publisher
- ACM
- Published
- 2016-09-15
- Pages
- 11-16
- OpenAlex
- W2471920251
- Cited by
- 169
- References
- 4
- FWCI
- 13.68
- Type
- conference paper
- Language
- en
- Open access
- green
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- Strub, F., Gaudel, R., & Mary, J. (2016). Hybrid Recommender System based on Autoencoders. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. https://doi.org/10.1145/2988450.2988456
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