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.
@article{arxiv.2405.11441,
title = {EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations},
author = {Chiyu Zhang and Yifei Sun and Minghao Wu and Jun Chen and Jie Lei and Muhammad Abdul-Mageed and Rong Jin and Angli Liu and Ji Zhu and Sem Park and Ning Yao and Bo Long},
journal= {arXiv preprint arXiv:2405.11441},
year = {2024}
}