The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Variational Autoencoders for Collaborative Filtering
Open accessAbstract
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.
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Cites
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
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
The information bottleneck method
Tishby, N., Pereira, F. C., & Bialek, W. (2000) · arXiv · doi:10.48550/arxiv.physics/0004057 · Cited by 2,626 · cited 1×
“The authors propose the deep variational information bottleneck, which is a variational approximation to the information bottleneck principle [46].”
BackgroundGenerating Sentences from a Continuous Space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1002 · Cited by 2,061
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
Bertin-Mahieux, T., Ellis, D. P. W., Whitman, B., & Lamere, P. (2011) · Columbia University · doi:10.7916/d8nz8j07 · Cited by 944
Cited by
Disentangled Graph Collaborative Filtering
Wang, X., Jin, H., Zhang, A., He, X., Xu, T., & Chua, T.-S. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401137 · Cited by 624
DDTCDR: Deep Dual Transfer Cross Domain Recommendation
Li, P., & Tuzhilin, A. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining · doi:10.1145/3336191.3371793 · Cited by 269
Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
Mchergui, A., Moulahi, T., & Zeadally, S. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100403 · Cited by 165
NSML: Meet the MLaaS platform with a real-world case study
Kim, H., Kim, M., Seo, D., Kim, J., Park, H., Park, S., Jo, H., Kim, K., Yang, Y., Kim, Y., et al. (2018) · arXiv · doi:10.48550/arxiv.1810.09957 · Cited by 91
Personality Bias of Music Recommendation Algorithms
Melchiorre, A. B., Zangerle, E., & Schedl, M. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3412223 · Cited by 48
Metadata
- Authors
- Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T.
- Venue
- Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 689-698
- OpenAlex
- W2963085847
- Cited by
- 1,338
- References
- 8
- FWCI
- 85.71
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018). Variational Autoencoders for Collaborative Filtering. Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18. https://doi.org/10.1145/3178876.3186150
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