• Corpus ID: 159042202

RaFM: Rank-Aware Factorization Machines

  title={RaFM: Rank-Aware Factorization Machines},
  author={Xiaoshuang Chen and Yin Zheng and Jiaxing Wang and Wenye Ma and Junzhou Huang},
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank for all features, this paper proposes a Rank-Aware FM (RaFM) model which adopts pairwise interactions from embeddings with different ranks. The proposed model achieves a better performance on real-world datasets where different features have significantly varying frequencies of occurrences. Moreover, we prove that… 

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Factorization Machines
  • Steffen Rendle
  • Computer Science
    2010 IEEE International Conference on Data Mining
  • 2010
Factorization Machines (FM) are introduced which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models and can mimic these models just by specifying the input data (i.e. the feature vectors).
Neural Factorization Machines for Sparse Predictive Analytics
NFM seamlessly combines the linearity of FM in modelling second- order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions, and is more expressive than FM since FM can be seen as a special case of NFM without hidden layers.