Paper Info
Title
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Abstract
Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large memory and computation cost. Even worse, useless interactions may introduce noise and complicate the training process. In this work, we propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS). AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. In the search stage, instead of searching over a discrete set of candidate feature interactions, we relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model. In the re-train stage, we keep the architecture parameters serving as an attention unit to further boost the performance. Offline experiments on three large-scale datasets (two public benchmarks, one private) demonstrate that AutoFIS can significantly improve various FM based models. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service, where a 10-day online A/B test demonstrated that AutoFIS improved the DeepFM model by 20.3% and 20.1% in terms of CTR and CVR respectively.
Year
DOI
Venue
2020
10.1145/3394486.3403314
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7998-4
6
PageRank 
References 
Authors
0.43
21
9
Name
Order
Citations
PageRank
Bin Liu14011.42
Zhu Chenxu260.43
guilin li3112.94
Weinan Zhang4122897.24
Lai Jincai560.43
Ruiming Tang612519.25
Xiuqiang He731239.21
Zhenguo Li858141.17
Yong Yu97637380.66