因子分解机(FM)家族里有哪些成员

我大知乎打公式太费劲了,就直接上截图了!有不当的地方,直接拍砖就行

图1 LR模型
图2 poly2模型
图3 FM模型
图4 xi的embedding
图5 FFM模型
图6 FwFM模型
图7 Bi-FM模型
图8 Wide&Deep模型
图9 FNN模型
图10 Deep Crossing模型
图11 NFM模型
图12 FNFM模型
图13 AFM模型
图14 PNN模型
图15 DeepFM模型
图16 DCN模型
图17 Cross Network特征交叉
图18 xDeepFM模型
图19 CIN特征交叉

参考文献

[1] Richardson M, Dominowska E, Ragno R. Predicting clicks: estimating the click-through rate for new ads[C]. Proceedings of the 16th international conference on World Wide Web. ACM, 2007: 521-530.

[2] Chang Y W, Hsieh C J, Chang K W, et al. Training and testing low-degree polynomial data mappings via linear SVM[J]. Journal of Machine Learning Research, 2010, 11(Apr): 1471-1490.

[3] Rendle S. Factorization machines[C].2010 IEEE International Conference on Data Mining. IEEE, 2010: 995-1000.

[4] Juan Y, Zhuang Y, Chin W S, et al. Field-aware factorization machines for CTR prediction[C].Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 43-50.

[5] Pan J, Xu J, Ruiz A L, et al. Field-weighted factorization machines for click-through rate prediction in display advertising[C].Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018: 1349-1357.

[6] infoq.cn/article/vKoKh_

[7] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]. Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 2016: 7-10.

[8] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C].European conference on information retrieval. Springer, Cham, 2016: 45-57.

[9] Shan Y, Hoens T R, Jiao J, et al. Deep crossing: Web-scale modeling without manually crafted combinatorial features[C].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 255-262.

[10] 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.

[11] 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.

[12] Zhang L, Shen W, Li S, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1902.09096, 2019.

[13] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.

[14] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C].2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016: 1149-1154.

[15] Guo H, Tang R, Ye Y, et al. DeepFM: a factorization-machine based neural network for CTR prediction[J]. arXiv preprint arXiv:1703.04247, 2017.

[16] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C].Proceedings of the ADKDD'17. ACM, 2017: 12.

[17] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining explicit and implicit feature interactions for recommender systems[C].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1754-1763.

[18] Liu H, He X, Feng F, et al. Discrete factorization machines for fast feature-based recommendation[J]. arXiv preprint arXiv:1805.02232, 2018.

[19] Blondel M, Fujino A, Ueda N, et al. Higher-order factorization machines[C].Advances in Neural Information Processing Systems. 2016: 3351-3359.

[20] Punjabi S, Bhatt P. Robust factorization machines for user response prediction[C].Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018: 669-678.

[21] Ma C, Liao Y, Wang Y, et al. F2M: Scalable Field-Aware Factorization Machines[J]. Proc. of the MLSys on NIPS, 2016.

[22] Li M, Liu Z, Smola A J, et al. Difacto: Distributed factorization machines[C].Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 2016: 377-386.

[23] Luo L, Zhang W, Zhang Z, et al. Sketched follow-the-regularized-leader for online factorization machine[C].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1900-1909.

发布于 2019-06-23