EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
<|reference_start|>EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations: Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world. In this work, we introduce EmbSum, a novel framework that enables offline pre-computations of users and candidate items while capturing the interactions within the user engagement history. By utilizing the pretrained encoder-decoder model and poly-attention layers, EmbSum derives User Poly-Embedding (UPE) and Content Poly-Embedding (CPE) to calculate relevance scores between users and candidate items. EmbSum actively learns the long user engagement histories by generating user-interest summary with supervision from large language model (LLM). The effectiveness of EmbSum is validated on two datasets from different domains, surpassing state-of-the-art (SoTA) methods with higher accuracy and fewer parameters. Additionally, the model's ability to generate summaries of user interests serves as a valuable by-product, enhancing its usefulness for personalized content recommendations.<|reference_end|>
@article{zhang2024embsum:,
    title={EmbSum: Leveraging the Summarization Capabilities of Large Language
  Models for Content-Based Recommendations},
    author={Chiyu Zhang, Yifei Sun, Minghao Wu, Jun Chen, Jie Lei, Muhammad
  Abdul-Mageed, Rong Jin, Angli Liu, Ji Zhu, Sem Park, Ning Yao, Bo Long},
    journal={arXiv preprint arXiv:2405.11441},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11441},
    primaryClass={cs.IR cs.CL}
}