The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
Abstract
Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.
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Cites
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Matrix Factorization Techniques for Recommender Systems
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ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Seung, D., & Lee, L.-W. (2001) · Cited by 4,753
Factorization meets the neighborhood: a multifaceted collaborative filtering model
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Cited by
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
Deep Item-based Collaborative Filtering for Top-N Recommendation
Xue, F., He, X., Wang, X., Xu, J., Liu, K., & Hong, R. (2019) · ACM Transactions on Information Systems · doi:10.1145/3314578 · Cited by 339
MIDA: Multiple Imputation Using Denoising Autoencoders
Gondara, L., & Wang, K. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93040-4_21 · Cited by 292
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems
Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., & Zhang, F. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10747 · Cited by 282
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Wang, H., & Yeung, D.-Y. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2606428 · Cited by 253
Metadata
- Authors
- Li, S., Kawale, J., & Fu, Y.
- Venue
- Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2015-10-17
- Pages
- 811-820
- OpenAlex
- W2038585576
- Cited by
- 422
- References
- 40
- FWCI
- 27.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, S., Kawale, J., & Fu, Y. (2015). Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. https://doi.org/10.1145/2806416.2806527
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