Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.

翻译：对话推荐系统正成为一种强大的工具，用于个性化用户的推荐体验。通过来回对话，用户可以快速精准地找到所需物品。然而，许多对话推荐方法仅部分探索了用户偏好空间，并对如何最佳整合用户反馈做出了限制性假设，导致对话冗长且推荐性能不佳。本文提出了一种新颖的对话推荐框架，具有两个独特特性：（i）贪婪NDCG属性选择器，通过在交互式偏好引导过程中优先选择最能有效代表用户实际偏好空间的属性，以增强用户个性化；（ii）用户表示精炼器，用于有效融合从交互式引导过程中收集的用户偏好，从而获得对用户更具个性化的理解。通过四个常用数据集上的大量实验，我们发现该框架不仅在推荐性能和对话效率方面优于所有最先进的对话推荐系统，还在所提出的多真实标签多轮对话推荐设置下为用户提供了更个性化的体验。