Building Personalized Recommender Systems with Milvus and PaddlePaddle

This post details the integration of Milvus with PaddlePaddle for building personalized recommender systems. It outlines the data preparation using the MovieLens Million Dataset, the Fusion Recommendation Model implemented in PaddlePaddle, and the system overview where movie feature vectors are stored in Milvus and user features are used for similarity search. It provides steps for model training, data preprocessing, and implementing the recommender system with Milvus, demonstrating how to insert movie feature vectors and perform similarity retrieval using user features.