Building Personalized Recommender Systems with Milvus and PaddlePaddle

This article discusses the creation of personalized recommender systems using Milvus and PaddlePaddle. The recommendation system is designed to help users find relevant information or products by analyzing their historical behavior. The MovieLens Million Dataset (ml-1m) is used as an example, which contains 1 million reviews of 4000 movies by 6000 users. A fusion recommendation model is implemented using PaddlePaddle's deep learning platform, and the movie feature vectors generated by the model are stored in Milvus, a vector similarity search engine. The user features are used as target vectors for searching within Milvus to obtain recommended movies. The main process involves training the model, preprocessing data, and implementing the personalized recommender system with Milvus. This combination of technologies allows for efficient and accurate recommendations based on user interests and needs.