Improved negative sampling method in collaborative filtering recommendation based on Generative adversarial network

Abstract

ObjectiveThe problem of low model performance caused by the lack of negative samples in the recommendation method based on implicit feedback information can be solved. MethodsThe implicit feedback recommendation model DAEGAN is constructed based on the conditional generative adversarial network framework. The Denoising Auto-Encoder is used as a generator to capture nonlinear potential factors in the interaction and improve the robustness of model. In this paper, a strong and weak negative sampling strategy is proposed, which combines the visibility of user in time points to mine uninteresting items and acquire strong negative samples, and injects these information into the model by modifying the masking mechanism to solve the problem of missing negative samples. ResultsExperiments on MovieLens 100 K, Amazon Movie and TV, MovieLens 1 M datasets show that the recommendation accuracy of CFGAN based on strong and weak negative sampling and DAEGAN proposed in this paper has been improved. LimitationsThe generation of strong negative samples is based on user interaction records, which cannot solve effectively cold start problems in extremely sparse data. ConclusionsAfter DAEGAN application, the strong and weak negative sampling method proposed in this paper has generally higher recommendation accuracy than those mainstream recommendation algorithms. The code is available at https://github.com/nanjingzhuyuxuan/DAEGAN.