关键词: 图卷积网络, 异构图学习, 压缩注意力机制, 推荐算法, 对比学习

Abstract: Aiming at the problems of incomplete information dimension, sparse interaction data and redundant interaction information in recipe recommendation tasks, a recipe recommendation model RecipeFlavor based on hierarchical learning of flavor embedding heterogeneous graph was proposed. First, the flavor molecule dimension was introduced, and a heterogeneous graph was constructed based on users, foods, ingredients and flavor substances of ingredients to effectively represent the connection between the four nodes. Then, a hierarchical learning module based on heterogeneous graph was constructed based on information transmission mechanism, and combined with compressed attention mechanism, different node relationships were regarded as different information channels, key interaction information between nodes was extracted and noise was suppressed. Finally, a contrastive learning module was constructed based on feature-aware noise, positive and negative sample discrimination tasks were introduced in model learning to enhance the information association between users and recipe nodes and improved the model's learning ability for features. Compared with HGAT (Hierarchical Graph Attention Network for Recipe Recommendation), the Area Under the Curve (AUC) of the RecipeFlavor model on the Recipe 1M+ large dataset increased by 1.44 percentage points, and the model Precision (Pre), Hit Rate (HR), Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG) of Top@10 increased by 0.76, 6.11, 2.68 and 3.05 percentage points respectively. Experimental results show that the introduction of flavor molecule information expands the learning dimension of recipe recommendation, and the RecipeFlavor model can effectively extract key information in heterogeneous graphs, enhance the correlation between users and recipes, and thus improve the accuracy of recipe recommendations.

Key words: Graph Convolution Network, Heterogeneous Graph Learning, Squeeze Attention Mechanism, Recommendation Algorithm, Contrastive Learning