[Blog] 📚 Accelerating candidate generation in recommender systems

If you have experience developing a recommender system, you are likely to have fallen victim to at least one of the following:

  • The system is extremely slow when returning results due to the tremendous amount of datasets.
  • Newly inserted data cannot be processed in real-time for search or query.
  • Deployment of the recommender system is daunting.

So how can you build a product recommender system designed for massive amounts of data with ease? This article provides a solution to accelerating the candidate generation process by using MIND, PaddlePaddle & Milvus.

Read more :point_right:t2: https://milvus.io/blog/2021-11-26-accelerating-candidate-generation-in-recommender-systems-using-milvus-paired-with-paddlepaddle.md?page=1#all