Data Collaborative Contrastive Recommendation model with self-adaptive noise

The recommender system recommends items to the users based on their preferences of implicit feedback. However, implicit feedback often contains noise that deviates from the user's true preferences, thereby influencing the accuracy of the recommendations. The most effective denoising method is Self-Guided Denoising Learning (SGDL), providing a general denoising scheme that can be applied to various recommendation models. However, it is typically hard to capture user preferences efficiently and enhance the diversity of recommendations to mitigate the 'filter bubble' phenomenon. To address these challenges, we propose a novel Data Collaborative Contrastive Recommendation model with self-adaptive noise (DCCR). Specifically, we design the informative item extraction module to mine informative items from the original interactions and improve the accuracy and diversity of recommendations by collaborative training of the informative and the original dataset to learn diverse user embeddings and adapt to noise. Extensive experiments on three public datasets demonstrate the superiority of our DCCR over state-of-the-art methods, balancing the diversity of recommendation lists while optimizing recommendation accuracy.