Learning Robust Recommenders through Cross-Model Agreement

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy examples are prevalent in real-world implicit feedback. A noisy positive example could be interacted but it actually leads to negative user preference. A noisy negative example which is uninteracted because of user unawareness could also denote potential positive user preference. Conventional training methods overlook these noisy examples, leading to sub-optimal recommendations. In this work, we propose a general framework to learn robust recommenders from implicit feedback. Through an empirical study, we find that different models make relatively similar predictions on clean examples which denote the real user preference, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose denoising with cross-model agreement (DeCA) which minimizes the KL-divergence between the real user preference distributions parameterized by two recommendation models while maximizing the likelihood of data observation. We instantiate DeCA on four representative recommendation models, empirically demonstrating its superiority over normal training and existing denoising methods. Codes are available at https://github.com/wangyu-ustc/DeCA .