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

Abstract

Objective

The problem of low model performance caused by the lack of negative samples in the recommendation method based on implicit feedback information can be solved.

Methods

The 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.

Results

Experiments 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.

Limitations

The generation of strong negative samples is based on user interaction records, which cannot solve effectively cold start problems in extremely sparse data.

Conclusions

After 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.