Semantic video content search and recommendation

The rapid growth of video streaming platforms has intensified the demand for personalized content recommendations. However, current solutions often rely on historical user data, leading to challenges like the cold start problem and overlooking users’ immediate preferences. We present a conversational recommendation system that leverages large language models (LLMs) to generate keyword-based content and query descriptions. By integrating Retrieval-Augmented Generation (RAG), our system efficiently retrieves relevant content, independent of prior user interactions, and ensures consistent performance across languages. Preliminary testing shows our system outperforms the RAG baseline by up to 24% in less descriptive queries and demonstrates consistent performance across three languages. While the results are promising, further evaluation focusing on user interaction and satisfaction is necessary. Our approach can potentially be extended to other recommendation systems, offering broader applicability and enhanced content personalization.