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.

翻译：本文介绍Legommenders，这是一个专为基于内容的推荐设计的独特库，支持内容编码器与行为和交互模块的联合训练，从而将内容理解无缝整合到推荐流程中。Legommenders使研究人员能够在15个不同数据集上轻松创建和分析超过1,000种不同的模型。此外，该库支持将现代大型语言模型作为特征编码器和数据生成器进行集成，为开发最先进的推荐模型提供了一个强大平台，从而实现更个性化和高效的内容推荐。