  Recent advancements in Large Language Models (LLMs) have attracted
considerable interest among researchers to leverage these models to enhance
Recommender Systems (RSs). Existing work predominantly utilizes LLMs to
generate knowledge-rich texts or utilizes LLM-derived embeddings as features to
improve RSs. Although the extensive world knowledge embedded in LLMs generally
benefits RSs, the application can only take limited number of users and items
as inputs, without adequately exploiting collaborative filtering information.
Considering its crucial role in RSs, one key challenge in enhancing RSs with
LLMs lies in providing better collaborative filtering information through LLMs.
In this paper, drawing inspiration from the in-context learning and chain of
thought reasoning in LLMs, we propose the Large Language Models enhanced
Collaborative Filtering (LLM-CF) framework, which distils the world knowledge
and reasoning capabilities of LLMs into collaborative filtering. We also
explored a concise and efficient instruction-tuning method, which improves the
recommendation capabilities of LLMs while preserving their general
functionalities (e.g., not decreasing on the LLM benchmark). Comprehensive
experiments on three real-world datasets demonstrate that LLM-CF significantly
enhances several backbone recommendation models and consistently outperforms
competitive baselines, showcasing its effectiveness in distilling the world
knowledge and reasoning capabilities of LLM into collaborative filtering.
