A Neural Collaborative Filtering Recommendation Algorithm Based on Attention Mechanism and Contrastive Learning

Abstract

The neural collaborative filtering recommendation algorithm is widely used in recommendation systems, which further applies deep learning to recommendation systems. It is a universal framework in the neural collaborative filtering recommendation algorithm, however, it does not consider the impact of different features on recommendation results, nor does it consider the issues of data sparsity and long tail distribution of items. To solve the above problems, this paper proposes a recommendation algorithm based on attention mechanism and contrastive learning, which focuses on more important features through attention mechanism and increases the number of samples to achieve data augmentation through contrastive learning, therefore it improves model performance. The experimental results on two benchmark datasets show that the algorithm proposed in this paper has further improved recommendation performance compared to other benchmark algorithms.