论文笔记：Neural Factorization Machines for Sparse Predictive Analytics

hexiangnan
2017 Neural Factorization Machines for Sparse Predictive Analytics

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.

side Information
user side information
binary features

[1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364.

[2] He X, Liao L, Zhang H, et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017: 173-182.