Published April 1, 2024 | Version v1
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Unlocking Personalized Anime Recommendations: Langchain and LLM at the Forefront

Authors/Creators

  • 1. University of Pittsburgh
  • 2. University of Washington
  • 3. Miami University
  • 4. Columbia University
  • 5. Duke University

Description

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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References

  • [1] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749.
  • [2] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
  • [3] Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
  • [4] Kaiyu, W., Yumei, W., & Lin, Z. (2014, September). A new three-layer QoE modeling method for HTTP video streaming over wireless networks. In 2014 4th IEEE International Conference on Network Infrastructure and Digital Content (pp. 56-60). IEEE.
  • [5] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.
  • [6] He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182).
  • [7] Covington, P., Adams, J., & Sargin, E. (2016, September). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 191-198).
  • [8] He, X., Du, X., Wang, X., Tian, F., Tang, J., & Chua, T. S. (2018). Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912.
  • [9] Mnih, A., & Salakhutdinov, R. R. (2007). Probabilistic matrix factorization. Advances in neural information processing systems, 20.
  • [10] Wang, H., Wang, N., & Yeung, D. Y. (2015, August). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1235-1244).
  • [11] Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.
  • [12] Bell, R. M., & Koren, Y. (2007). Lessons from the Netflix prize challenge. Acm Sigkdd Explorations Newsletter, 9(2), 75-79.
  • [13] Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 76-80.
  • [14] Wang, K., Wang, Y., Li, H., Xiong, Y., & Zhang, X. (2014, May). A new approach for detecting spam microblogs based on text and user's social network features. In 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE) (pp. 1-5). IEEE.
  • [15] Salakhutdinov, R., & Mnih, A. (2008, July). Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th international conference on Machine learning (pp. 880-887).
  • [16] Wang, J., Yu, L., Zhang, W., Gong, Y., Xu, Y., Wang, B., ... & Zhang, D. (2017, August). Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 515-524).
  • [17] Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38.
  • [18] Wang, Y., Sun, M., Wang, K., & Zhang, L. (2016). Quality of experience estimation with layered mapping for hypertext transfer protocol video streaming over wireless networks. International Journal of Communication Systems, 29(14), 2084-2099.
  • [19] Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008, December). One-class collaborative filtering. In 2008 Eighth IEEE international conference on data mining (pp. 502-511). IEEE.
  • [20] Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining (pp. 263-272). Ieee.
  • [21] Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... & Shah, H. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems (pp. 7-10).
  • [22] Cheng, S., Chu, B., Zhong, B., Zhang, Z., Liu, X., Tang, Z., & Li, X. (2021). DRNet: Towards fast, accurate and practical dish recognition. Science China Technological Sciences, 64(12), 2651-2661.
  • [23] Yan, X., Xiao, M., Wang, W., Li, Y., & Zhang, F. (2024). A Self-Guided Deep Learning Technique for MRI Image Noise Reduction. Journal of Theory and Practice of Engineering Science, 4(01), 109-117.
  • [24] Zhang, Z., Zhong, B., Zhang, S., Tang, Z., Liu, X., & Zhang, Z. (2021). Distractor-aware fast tracking via dynamic convolutions and mot philosophy. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1024-1033).
  • [25] Weimin, W. A. N. G., Yufeng, L. I., Xu, Y. A. N., Mingxuan, X. I. A. O., & Min, G. A. O. (2024). Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion. International Journal of Innovative Research in Computer Science & Technology, 12(1), 26-34.
  • [26] Pan, Zhenyu, et al. "CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver." arXiv preprint arXiv:2403.03391 (2024).
  • [27] Hu, Zhirui, et al. "On the design of quantum graph convolutional neural network in the nisq-era and beyond." 2022 IEEE 40th International Conference on Computer Design (ICCD). IEEE, 2022.