The integration of large language models (LLMs) into recommender systems has garnered attention due to their ability to leverage rich semantic representations. However, these models suffer from a limitation, knowledge bias, a systematic tendency to over-recommend items dominated by pretraining knowledge, regardless of actual user interests. Although recent studies have focused on fine-tuning LLMs to better align with the collaborative data, our investigation reveals that such fine-tuning fails to fundamentally correct this deep-seated bias. In this work, we propose ReKnow, a lightweight and efficient decoding-time alignment method to mitigate knowledge bias. Specifically, we quantify LLMs’ knowledge towards biased items, then realign output probabilities to match the target data distribution. For validation, we provide both theoretical justifications and empirical results, including evaluation on two datasets, demonstrating that our  enhances the quality and diversity of LLM-based sequential recommendation. We have release our code[<https://anonymous.4open.science/r/ReKnow-B17E>] for others to reproduce the results.