  Collaborative filtering recommender systems (CF-RecSys) have shown successive
results in enhancing the user experience on social media and e-commerce
platforms. However, as CF-RecSys struggles under cold scenarios with sparse
user-item interactions, recent strategies have focused on leveraging modality
information of user/items (e.g., text or images) based on pre-trained modality
encoders and Large Language Models (LLMs). Despite their effectiveness under
cold scenarios, we observe that they underperform simple traditional
collaborative filtering models under warm scenarios due to the lack of
collaborative knowledge. In this work, we propose an efficient All-round
LLM-based Recommender system, called A-LLMRec, that excels not only in the cold
scenario but also in the warm scenario. Our main idea is to enable an LLM to
directly leverage the collaborative knowledge contained in a pre-trained
state-of-the-art CF-RecSys so that the emergent ability of the LLM as well as
the high-quality user/item embeddings that are already trained by the
state-of-the-art CF-RecSys can be jointly exploited. This approach yields two
advantages: (1) model-agnostic, allowing for integration with various existing
CF-RecSys, and (2) efficiency, eliminating the extensive fine-tuning typically
required for LLM-based recommenders. Our extensive experiments on various
real-world datasets demonstrate the superiority of A-LLMRec in various
scenarios, including cold/warm, few-shot, cold user, and cross-domain
scenarios. Beyond the recommendation task, we also show the potential of
A-LLMRec in generating natural language outputs based on the understanding of
the collaborative knowledge by performing a favorite genre prediction task. Our
code is available at https://github.com/ghdtjr/A-LLMRec .
