Dailymotion's machine learning engineers, Gladys Roch and Samuel Leonardo Gracio, discuss their use of Qdrant, a vector search engine, to enhance video recommendation systems by addressing challenges such as cold start issues and scalability. They detail how Qdrant allows them to perform fast neighbor searches using a Python API and metadata filtering, which is crucial for multilingual recommendations. The implementation of Qdrant has significantly improved the click-through rate (CTR) for videos with few interactions, thereby increasing user engagement with fresh and niche content. The engineers also highlight the importance of textual metadata, like transcripts, over video signals for creating robust video embeddings. Additionally, they mention plans to expand Qdrant's use to other projects, such as a feature called "Perspective," which aims to diversify user experiences by offering varied viewpoints on similar topics.