Attention-Based Graph Convolution Collaborative Filtering

The development of big data has brought changes to society and brought us challenges. How to extract useful information from complex data has become the focus of research in recent years. Personalized recommendation as an effective solution has received widespread attention in academia and industry. Collaborative filtering has been widely used by finding users with similar user behaviors as the preferences of similar users. However, the existing methods ignore the interaction information between the user and the item during feature extraction, which leads to imperfect feature extraction and affects the algorithm effect. This paper proposes a graph convolution collaborative filtering model based on the attention model, which uses the graph convolution network to embed user and item interaction information into feature vectors, and uses the attention model to highlight the relatively important interaction information among them, so as to obtain more excellent feature vector. The experimental results show that the model has a good effect on the two commonly used evaluation metrics: recall and normalized discounted cumulative gain(NDCG).