A Graph Neural Network-Based Framework for Personalized Online Grocery Recommendation

This paper presents an online grocery recommendation system using Graph Neural Recommenders. By modeling user-product interactions as a graph, the system can learn complex relationships and provide personalized recommendations. The proposed approach improves recommendation quality and enhances the online shopping experience. Future work can focus on integrating real-time user behavior and expanding the system to handle larger datasets for more accurate predictions.

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