CHIANGEL/Awesome-LLM-for-RecSys

What it solves

This project addresses the challenge of keeping up with the rapidly evolving research on integrating Large Language Models (LLMs) into recommender systems. It provides a curated, organized repository of academic papers and resources that explore how LLMs can improve recommendation tasks.

How it works

The repository categorizes research papers based on the specific role the LLM plays within a recommendation pipeline. These categories include using LLMs for feature engineering, acting as feature encoders to create better data representations, and serving as scoring or ranking functions to predict user preferences.

Who it’s for

This collection is designed for researchers, data scientists, and developers who are interested in the intersection of generative AI and recommendation technology. It serves as a central reference point for those looking to understand current methodologies, tuning strategies, and the state-of-the-art in LLM-enhanced recommendation systems.

Highlights

• A comprehensive, categorized list of academic papers on LLM-based recommendation.
• Includes a survey paper accepted by ACM Transactions on Information Systems (TOIS).
• Provides a structured taxonomy of how LLMs are adapted for recommendation pipelines.
• Regularly updated with new research works and includes a link to weekly paper notes.