Abstract:

Although traditional recommendation systems use graph structure information, most of them only consider the basic attributes of users and items, ignoring the important factor of contextual interaction information between users and items. Even if contextual interaction information is taken into account, there is a lack of attention in the layer combination stage. force mechanism to assign weight. To solve this problem, a CIAGCN (Context Information Attention Graph Convolutional Networks) recommendation model that integrates contextual interactive information and attention mechanism is proposed. This model utilizes the contextual interaction

information of users and items while applying the high-order connectivity theory of graphs to obtain deeper collaborative signals. An attention mechanism is introduced in the layer combination stage to improve the interpretability of this stage. The model was experimentally compared on the Yelp-OH, Yelp-NC and Amazon- Book data sets. The results showed that the model had a certain effect compared with other algorithms, indicating that the recommendation effect was better than some traditional recommendation models.

Key words: attention mechanism, recommendation system, bipartite graph, graph neural network