Feature-based matrix factorization

Feature-based matrix factorization is an abstract matrix factorization model that uses 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.

Related Models

• Factorization Machine: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features, allowing us to overcome some shortcomings of FM(e.g: including neighborhood information, exact implementation of rank,svd++ ). We call the model feature-based matrix factorization instead of restricted FM because the idea descends more naturally from matrix factorization.

Implementation

• SVDFeature is an efficient and scalable implementation of feature-based matrix factorization.

References

• Tianqi Chen, Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271