# Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support

arXiv: https://arxiv.org/abs/2412.15973 · HTML: https://mlsanity.com/paper/2412.15973/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [Efficient Content-based Recommendation Model Training via Noise-aware Coreset Selection](https://mlsanity.com/paper/2601.10067/) (2026)

## Abstract (arXiv)

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.