Collaborative Filtering Algorithm Based on Generative Adversarial Networks

Traditional recommendation algorithms, such as collaborative filtering, rely on users' historical behavior and similarity with other users to predict their interests and provide personalized recommendations for content or products. While they can meet certain accuracy requirements for recommendations, recommendation systems still encounter various challenges that are difficult to overcome, such as the cold start problem and data sparsity. As a result, neural network-based recommendation systems have emerged. Generative adversarial networks (GANs) are machine learning methods that learn through competition and can generate data that conforms to a certain distribution. Attention mechanisms consider the relationships between each element and other elements in the input sequence, thereby enhancing the expressive power of the model. The proposed approach combines these two mechanisms in a neural network architecture. Experimental results have shown that this algorithm can enhance the accuracy of recommendation results.