The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
Abstract
Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide context-aware recommendations.
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Cites
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Advances in Neural Information Processing Systems 18
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Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
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Cited by
Tensor Decomposition for Signal Processing and Machine Learning
Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., & Faloutsos, C. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2690524 · Cited by 1,617
Recurrent knowledge graph embedding for effective recommendation
Sun, Z., Yang, J., Zhang, J., Bozzon, A., Huang, L.-K., & Xu, C. (2018) · Proceedings of the 12th ACM Conference on Recommender Systems · doi:10.1145/3240323.3240361 · Cited by 367
Improving pairwise learning for item recommendation from implicit feedback
Rendle, S., & Freudenthaler, C. (2014) · Proceedings of the 7th ACM international conference on Web search and data mining · doi:10.1145/2556195.2556248 · Cited by 360
Sequential User-based Recurrent Neural Network Recommendations
Donkers, T., Loepp, B., & Ziegler, J. (2017) · Proceedings of the Eleventh ACM Conference on Recommender Systems · doi:10.1145/3109859.3109877 · Cited by 359
Chen, R., Hua, Q., Chang, Y.-S., Wang, B., Zhang, L., & Kong, X. (2018) · IEEE Access · doi:10.1109/access.2018.2877208 · Cited by 312
Metadata
- Authors
- Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N.
- Venue
- Proceedings of the fourth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 79-86
- OpenAlex
- W2102937240
- Cited by
- 721
- References
- 18
- FWCI
- 16.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. Proceedings of the fourth ACM conference on Recommender systems. https://doi.org/10.1145/1864708.1864727
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