  Large Language Models (LLMs) have demonstrated great potential in
Conversational Recommender Systems (CRS). However, the application of LLMs to
CRS has exposed a notable discrepancy in behavior between LLM-based CRS and
human recommenders: LLMs often appear inflexible and passive, frequently
rushing to complete the recommendation task without sufficient inquiry.This
behavior discrepancy can lead to decreased accuracy in recommendations and
lower user satisfaction. Despite its importance, existing studies in CRS lack a
study about how to measure such behavior discrepancy. To fill this gap, we
propose Behavior Alignment, a new evaluation metric to measure how well the
recommendation strategies made by a LLM-based CRS are consistent with human
recommenders'. Our experiment results show that the new metric is better
aligned with human preferences and can better differentiate how systems perform
than existing evaluation metrics. As Behavior Alignment requires explicit and
costly human annotations on the recommendation strategies, we also propose a
classification-based method to implicitly measure the Behavior Alignment based
on the responses. The evaluation results confirm the robustness of the method.
