整合LLM的推荐系统库，支持千种模型与15个数据集
Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support
- 内容编码器与行为模块联合训练，实现内容理解无缝融入推荐流程
- 支持超1000种模型配置，在15个数据集上可快速构建实验
- 兼容大语言模型作特征编码或数据生成，适合个性化推荐研究
我们提出Legommenders，一个专为基于内容的推荐设计的综合性库，支持内容编码器与行为、交互模块的联合训练，使内容理解可直接融入推荐流程。该库允许研究人员在15个不同数据集上轻松创建并分析超过1000种不同模型。此外，它支持将现代大型语言模型作为特征编码器或数据生成器使用，为构建前沿推荐模型提供强大平台，提升推荐的个性化与有效性。
原文摘要 · Abstract (English)
We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules, thereby facilitating the seamless integration of content understanding directly into the recommendation pipeline. Legommenders allows researchers to effortlessly create and analyze over 1,000 distinct models across 15 diverse datasets. Further, it supports the incorporation of contemporary large language models, both as feature encoder and data generator, offering a robust platform for developing state-of-the-art recommendation models and enabling more personalized and effective content delivery.
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