Difference between revisions of "Feature-based matrix factorization"
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* [[User:Tqchen | Tianqi Chen]], Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271 | * [[User:Tqchen | Tianqi Chen]], Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271 | ||
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Revision as of 20:11, 23 September 2011
Feature-based matrix factorization is an abstract matrix factorization model that use features to describe the global bias and user/item factors. The the model allows development of new model simply by feature defining. We can incorporate information such as temporal information, neighborhood information, taxonomy information into feature-based matrix factorization to make the model informative. If we have a solver for feature-based matrix factorization, we only need to design context-aware or informative collaborative filtering(or ranking) models by feature-defining, without engineering efforts for writing codes for each new model.
Implementation
- SVDFeature is an efficient and scalable implementation of feature-based matrix factorization.
Related Models
- Factorization Machine: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features.
References
- Tianqi Chen, Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271
