AutoDenoise: Automatic Data Instance Denoising for Recommendations

Understand

Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences.

• However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests.
• While a large number of denoising studies are emerging in the recommender system community, all of them suffer from highly dynamic data distributions.
• In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems.