Unlocking Personalized Anime Recommendations: Langchain and LLM at the Forefront
DOI:
https://doi.org/10.5281/zenodo.10878197Keywords:
Recommendation System, Langchain, Large Language Models, Data Analysis, Vector Database, Prompt EngineeringAbstract
This paper introduces an innovative recommendation system that leverages Langchain and Large Language Models (LLMs) to provide tailored anime suggestions. By employing a sophisticated data analysis and model training framework, the system significantly enhances the accuracy and relevance of recommendations. Utilizing a vector database for efficient similarity searches and a novel approach to prompt engineering, the system adeptly interprets user preferences, thereby delivering personalized content recommendations. The integration of Langchain with LLMs showcases a significant advancement in the application of AI-driven techniques in recommendation systems. Our findings indicate that the proposed system not only improves recommendation quality but also offers insights into the effective utilization of language models and retrieval-based QA in the domain of personalized entertainment.
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