Traditional collaborative filtering (CF) techniques often encounter challenges with sparse datasets and cold-start issues. This paper introduces a plus version of traditional reliable Collaborative Filtering recommendation ACTivated by large language model named “ACT-CF” that integrates LLM with UserCF. ACT-CF employs LLM to generate predicted rating data, operates through an online-offline workflow to leverage the strengths of UserCF for delivering real-time recommendations and utilizing LLM to address cold-start scenarios. Extensive experiments demonstrate that our approach outperforms traditional methods in terms of effectiveness, computational speed, and system stability.