Contrastive knowledge graph attention network for request-based recipe recommendation
To improve daily customer experience, kitchen assistant becomes one of the enabled service in intelligent voice assistants, presenting personalized and relevant recipes to satisfy customer requests. Current solutions for recipe recommendation suffers from two limitations: First, user-recipe interactions are modeled in a uniform manner, which neglects the diversity of user preferences on recipe adoptions, inherently hurting the model performance. Second, users may interact with recipe randomly, resulting in inevitable data noise issue. In this work, we alleviate the foregoing issues by proposing contrastive knowledge graph attention network for recipe recommendation, where a knowledge graph attention-based recommender helps learn fine-grained user and recipe embeddings by modeling diversified user preferences from user behaviors. Moreover, a contrastive learning module that integrates unsupervised and supervised contrastive learning is proposed to improve model robustness. The experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art baseline methods.