Analyzing Data Augmentation Techniques for Contrastive Learning in Recommender Models

This paper investigates the application of contrastive learning-based user and item representation learning in recommendation systems. A recommendation model combining contrastive loss with data augmentation strategies is proposed. Traditional recommendation systems typically rely on explicit or implicit feedback to model the relationships between users and items. However, traditional methods often show limited performance when dealing with issues such as data sparsity and cold start. To address this, the paper introduces a contrastive learning framework. By constructing positive and negative sample pairs, the model is guided to learn more discriminative representations. Various data augmentation methods are also applied to enhance the robustness and generalization capability of representation learning. Specifically, the paper compares different data augmentation strategies, including subsampling views, feature masking, and behavioral perturbation, and analyzes their performance under different temperature parameters and sparsity conditions. The experimental results show that the contrastive learning-based model effectively improves recommendation accuracy, particularly in addressing sparse data and cold start problems. Additionally, the paper explores the performance differences of the contrastive loss function under different training settings, validating the significant impact of appropriate hyperparameter tuning on recommendation system performance. By combining contrastive learning with data augmentation, this study provides a new perspective and significantly enhances the performance of recommendation systems in complex scenarios. The research offers both theoretical support and practical guidance for the future development of personalized recommendation technologies.