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

翻译：大语言模型（LLMs）正通过精准索引物品内容、理解复杂对话上下文以及生成相关物品标题，彻底改变对话推荐系统。然而，控制推荐物品的分布仍是一大挑战。由于未能捕捉目标对话推荐平台上快速变化的数据分布（如物品流行度），这导致性能次优。在对话推荐中，LLMs通过自回归生成标题（作为多个词元）来推荐物品，这使得获取和控制所有物品上的推荐结果变得困难。因此，我们提出“重新索引再适配”（Reindex-Then-Adapt, RTA）框架，该框架将多词元物品标题转换为LLMs中的单词元，并相应调整这些单词物品标题上的概率分布。RTA框架融合了LLMs与传统推荐系统（RecSys）的优势：既能像LLMs一样理解复杂查询，又能像传统RecSys一样在对话推荐中高效控制推荐物品分布。我们的框架在三个不同的对话推荐数据集和两种适配设置下均展现出改进的准确率指标。