Uniform Regularization and Constrative Learning to Mitigate the Long-Tail Effect of Recommendation Algorithms

In recommender systems, Graph Collaborative Filtering (GCF) is widely used for its ability to effectively model the interaction between users and items. However, in practical scenarios, GCF faces a significant problem: the representation of popular items tends to be over-concentrated, while cold items are marginalized, leading to recommendation results biased towards popular items and making it difficult to address the issue of long-tail distribution. To alleviate data sparsity, existing GCF methods typically incorporate Contrastive Learning (CL) to assist in updating node representations. However, inappropriate CL methods can introduce extra noise. For this reason, this paper proposes an Enhanced Contrastive Learning-based Graph Collaborative Filtering (ECL-GCF). The model improves the traditional GCF approach by: 1. capturing explicit interaction information between users and items by exploiting structural neighborhood contrastive learning; 2. introducing semantic neighborhood contrastive learning to construct potential similarity relationships by capturing implicit semantic information of users and items, thereby providing more meaningful representations for cold items; and 3. optimizing the embedding representation by regularizing chi-square and homogeneous embedding representations, ensuring that the embeddings are both close to positive sample pairs and uniformly distributed in the space, thus preventing the marginalization of cold items. Experimental results indicate that the model improves recommendation performance by approximately 5% on the Yelp2018 and iFashion datasets, and especially performs well on cold item recommendation.