A Case Study of a Personalized Movie Guide Platform using RAG-based information

Recently, as domestic movie information platform is rapidly reorganized due to the OTT, the number of out-of-theater viewings is rapidly increasing. However, existing movie information platforms only provide fragmented ratings or fragmented reviews, and do not have the ability to integrate and summarize vast online information, making it difficult for users to find the information they need. This leads to a lack of reliable information, making movie selection even more difficult. To solve these problems and provide a personalized movie information search experience, we propose a movie guide platform based on Retrieval Augmented Generation (RAG). This platform dynamically searches for the latest information from a large knowledge base and analyzes user viewing history and preferred genres to recommend personalized movies. In addition, it generates meaningful answers to complex questions through context-based Q&A. It is expected to provide a movie search and selection experience optimized for changing movie consumption patterns and high satisfaction through a RAG-based chatbot, and we aim to improve expandability to various domains and data processing efficiency in the future.