Major online video platforms like YouTube, which receives 500 hours of user generated content each minute, present unique demands when it comes to content recommendation. In order to make relevant, real-time recommendations that take into consideration new uploads, video recommender systems must offer lightning-fast query time and efficient dynamic data handling. By converting key frames into vectors and then feeding the results into Milvus, billions of videos can be searched and recommended in near real time.
Visit the github repo to learn how to build a video similarity search engine using Milvus and a VGG neural network.
You can also learn more about how to use Milvus to build other systems for various application scenarios in our bootcamp repo on Github.