Unlocking Personalized Anime Recommendations: Langchain and LLM at the Forefront


  • Ye Zhang University of Pittsburgh
  • Kailin Gui University of Washington
  • Mengran Zhu Miami University
  • Yong Hao Columbia University
  • Haozhan Sun Duke University




Recommendation System, Langchain, Large Language Models, Data Analysis, Vector Database, Prompt Engineering


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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Author Biographies

Ye Zhang, University of Pittsburgh

University of Pittsburgh, USA, Email: yez12@pitt.edu

Kailin Gui, University of Washington

University of Washington, USA, Email:guikailin015@gmail.com

Mengran Zhu, Miami University

Miami University, USA, Email:mengran.zhu0504@gmail.com

Yong Hao, Columbia University

Columbia University, USA, Email:EricHao3290@gmail.com

Haozhan Sun, Duke University

Duke University, USA, Email:yzjshz1998@outlook.com


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Unlocking Personalized Anime Recommendations: Langchain and LLM at the Forefront




How to Cite

Y. Zhang, K. Gui, M. Zhu, Y. Hao, and H. Sun, “Unlocking Personalized Anime Recommendations: Langchain and LLM at the Forefront”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 2, pp. 46–53, Apr. 2024.