RecCocktail: A Generalizable and Efficient Framework for LLM-Based Recommendation
Min Hou, Chenxi Bai, Le Wu, Hao Liu, Kai Zhang, Weiwen Liu, Richang Hong, Ruiming Tang
Abstract
Large Language Models (LLMs) have achieved remarkable success in recent years, owing to their impressive generalization capabilities and rich world knowledge. To capitalize on the potential of using LLMs as recommender systems, mainstream approaches typically focus on two paradigms. The first paradigm designs multi-domain or multi-task instruction data for generalizable recommendation, so as to align LLMs with general recommendation areas and deal with cold-start recommendation. The second paradigm focuses on enhancing domain-specific recommendation tasks, improving performance in warm recommendation scenarios. While most previous works treat these two paradigms separately, we argue that they have complementary advantages, and combining them can yield better results. In this paper, we propose a generalizable and efficient LLM-based recommendation framework RecCocktail. Our approach begins with fine-tuning a "base spirit" LoRA module using domain-general recommendation instruction data to align LLM with recommendation knowledge. Next, given users
BibTeX
@inproceedings{aaai2026_reccocktailagene,
title = {RecCocktail: A Generalizable and Efficient Framework for LLM-Based Recommendation},
author = {Min Hou and Chenxi Bai and Le Wu and Hao Liu and Kai Zhang and Weiwen Liu and Richang Hong and Ruiming Tang and Meng Wang},
booktitle = {AAAI 2026},
year = {2026}
}