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

TL;DR

Legommenders is a versatile library that integrates content understanding into recommendation systems, supporting joint training of encoders and large language models for personalized content delivery across diverse datasets.

Contribution

It introduces a comprehensive library enabling joint training of content encoders with behavior modules and supports LLM integration for advanced recommendation modeling.

Findings

Supports over 1,000 models across 15 datasets

Facilitates integration of large language models as feature encoders and data generators

Enhances personalization and effectiveness in content recommendation

Abstract

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.