Optimizing User Experience: BIGO Leverages Milvus for Duplicate Video Removal

BIGO, the owner of short video platform Likee, has leveraged Milvus, an open-source vector database, to optimize its duplicate video removal process. With millions of daily uploads on Likee, the proliferation of duplicate videos posed a threat to content quality and user experience. Previously, BIGO used FAISS for similarity search but faced limitations in managing massive vectors. Milvus provided faster query responses and scalability, improving throughput and efficiency. The transformation involved converting new video frames into feature vectors and matching them against an extensive database of existing content using cutting-edge technologies like Kafka, deep learning models, and relational databases. BIGO plans to extend Milvus's capabilities for content moderation, restriction, and customized video services in the future.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.