• 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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  • Steffen Rendle
  • Computer Science
    2010 IEEE International Conference on Data Mining
  • 2010
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