Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, controlling the distribution of recommended items remains a challenge. This leads to suboptimal performance due to the failure to capture rapidly changing data distributions, such as item popularity, on targeted conversational recommendation platforms. In conversational recommendation, LLMs recommend items by generating the titles (as multiple tokens) autoregressively, making it difficult to obtain and control the recommendations over all items. Thus, we propose a Reindex-Then-Adapt (RTA) framework, which converts multi-token item titles into single tokens within LLMs, and then adjusts the probability distributions over these single-token item titles accordingly. The RTA framework marries the benefits of both LLMs and traditional recommender systems (RecSys): understanding complex queries as LLMs do; while efficiently controlling the recommended item distributions in conversational recommendations as traditional RecSys do. Our framework demonstrates improved accuracy metrics across three different conversational recommendation datasets and two adaptation settings

LLMs在会话推荐系统中通过熟练索引物品内容、理解复杂的对话环境和生成相关物品标题, 正在颠覆会话式推荐系统，然而，控制推荐物品的分布仍然是一个挑战。因此，我们提出了一个Reindex-Then-Adapt（RTA）框架，用于转换LLMs中的多令牌物品标题为单个令牌，并相应地调整这些单个令牌的概率分布，以有效地控制对话式推荐中的推荐物品分布。该框架结合了LLMs和传统推荐系统的优势，在理解LLMs中复杂的查询的同时，高效地控制推荐物品的分布。我们的框架在三个不同的会话式推荐数据集和两个适应设置中都展示出了改进的准确度指标。

重建-调整：改进面向对话推荐的大型语言模型