Rag Based Interactive Chatbot for Video Streaming Services

Abstract

The proliferation of content within video streaming services presents a significant challenge for users seeking personalized recommendations and specific information. This research addresses this challenge by developing a Retrieval-Augmented Generation (RAG) chatbotn designed to enhance user experience through conversational AI. The primary contribution of this work is a novel Retrieval-Augmented Generation (RAG) architecture featuring a dual-retrieval system that combines semantic search for descriptive requests and structured queries for fact based inquiries. This approach grounds the Large Language Model (LLM) in a factual knowledge base, mitigating the risk of hallucinations. The system is engineered to handle empty data retrieval scenarios by dynamically relaxing search filters, ensuring a robust user experience. The effectiveness of this RAG approach was validated through a comprehensive set of automated evaluations. The system demonstrates high precision in ranked list retrieval with questions like "Recommend me the top 5 action movies with highest IMDb scores", achieving an average NDCG@k of 0.837. While the chatbot shows strong semantic understanding by achieving 91% accuracy with contextual clues such as "Which Batman movies are directed by Christopher Nolan?", its performance with more ambiguous, plot-only queries (59.5% accuracy) indicates clear opportunities for future refinement. These results confirm that the dual-tool architecture successfully combines the flexibility of semantic search with the precision of structured queries, paving the way for more intuitive and efficient content discovery on streaming platforms. © 2025 IEEE.