Research on the application of attention mechanism based multi-model fusion in food recommendation platforms

Abstract

Smartphone-based food ordering has greatly enhanced convenience in daily life, and the rise of recommendation systems has transformed the functionality and user experience of food delivery applications. Innovations in recommendation algorithms and models have significantly improved the efficiency of food, merchant, and advertisement recommendations on food platforms, leading to higher transaction rates and greater user satisfaction. To further enhance recommendation efficiency, this study introduces a novel multi-model fusion recommendation architecture based on the multi-head self-attention mechanism, utilizing a two-tier structure. The first-tier model (the attention-based homogeneous AutoInt model) acts as a teacher to guide the training of the second-tier Transformer model. This hierarchical approach integrates multiple models through knowledge distillation, significantly improving the accuracy of the recommendation system. The complexity and performance of the proposed architecture were analyzed and applied in a production environment. Testing on a private dataset reveals that the proposed multi-model fusion recommendation architecture significantly enhances recommendation performance across various food platform scenarios, achieving an accuracy of 0.7643, recall of 0.8262, and an F1 score of 0.7936. These results surpass the performance of current state-of-the-art models. Therefore, the proposed architecture is not only highly applicable to food recommendation systems but also has broad applicability in other fields such as retail and entertainment.