Boosting Factorization Machines Via Saliency-Guided Mixup

Chenwang Wu, Defu Lian, Yong Ge, Min Zhou, Enhong Chen, Dacheng Tao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive features in sparse data, existing FMs can only estimate the parameters corresponding to these features via the inner product of their embeddings. Undeniably, they cannot learn the direct interactions of these features, which limits the model's expressive power. To this end, we first present MixFM, inspired by Mixup, to generate auxiliary training data to boost FMs. Unlike existing augmentation strategies that require labor costs and expertise to collect additional information such as position and fields, these augmented data are only by the convex combination of the raw ones without any professional knowledge support. More importantly, if non-interactive features exist in parent samples to be mixed respectively, MixFM will establish their direct interactions. Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM). Guided by the customized saliency, SMFM can generate more informative neighbor data. Through theoretical analysis, we prove that the proposed methods minimize the upper bound of the generalization error, which positively enhances FMs. Finally, extensive experiments on seven datasets confirm that our approaches are superior to baselines. Notably, the results also show that “poisoning” mixed data benefits the FM variants.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
DOIs
StateAccepted/In press - 2024
Externally publishedYes

Keywords

  • Adaptation models
  • Data models
  • Factorization machines
  • Feature extraction
  • Frequency modulation
  • Recommender systems
  • recommender systems
  • sparse data
  • Task analysis
  • Training

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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