Neural Factorization Machines for Sparse Predictive Analytics

@article{He2017NeuralFM,
  title={Neural Factorization Machines for Sparse Predictive Analytics},
  author={Xiangnan He and Tat-Seng Chua},
  journal={Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2017}
}
  • Xiangnan HeTat-Seng Chua
  • Published 7 August 2017
  • Computer Science
  • Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature vector highly sparse. To learn from such sparse data effectively, it is crucial to account for the interactions between features. Factorization Machines (FMs) are a popular… 

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