Graph Contrastive Recommendation Algorithm Based on Multimodal Feature Fusion*

Recommendation algorithms, as an extremely important information filtering and pushing tool, have been widely applied in many fields such as e-commerce, social networks, and online audio-visual entertainment. In research in recent years, multimodal recommendation algorithms that integrate multimodal information of items (such as text, images, etc.) and users' historical interaction information have achieved good results. However, most of the existing multimodal recommendation algorithms adopt the decision - level fusion method, this approach makes it difficult to capture the complementary information between modalities. Moreover, the few recommendation algorithms that adopt feature - level fusion have also failed to achieve good recommendation results. In view of this, we propose a feature-level fusion method based on cross-attention mechanism to capture the correlations between various modalities. In addition, in order to alleviate the problems of data sparsity and weak self-supervised signals faced by graph representation learning, we introduce graph contrastive learning into the model. At the same time, considering the long-tail problem of data and in order to avoid the uneven learning representation caused by the difference in graph structure between head data and tail data in traditional graph enhancement methods, we propose a long-tail graph enhancement method. We have carried out a large number of experiments on two real datasets and compared with other advanced baseline models. The results show that our model has been significantly improved in multiple evaluation indicators.