Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Despite the progress achieved in CRSs, existing solutions are far from satisfaction in the following two aspects: 1) current CRSs usually require each user to answer a quantity of clarifying questions before reaching the final recommendation, which harms the user experience; 2) there is a semantic gap between the learned representations of explicitly mentioned attributes and items. To address these drawbacks, we introduce the knowledge graph (KG) as the auxiliary information for comprehending and reasoning a user's preference, and propose a new CRS framework, namely Knowledge Enhanced Conversational Reasoning (KECR) system. As a user can reflect her/his preference via both attribute- and item-level expressions, KECR closes the semantic gap between two levels by embedding the structured knowledge in the KG. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user's feedback to clarifying questions. KECR can find a prominent reasoning chain to make the recommendation explainable and more rationale, as well as smoothen the conversation process, leading to better user experience and conversational recommendation accuracy. Extensive experiments on two real-world datasets demonstrate our approach's superiority over state-of-the-art baselines in both automatic evaluations and human judgments.

翻译：传统推荐系统仅基于历史交互记录估计用户对物品的偏好，因此无法捕捉细粒度且动态变化的用户兴趣，且用户仅能被动接收推荐结果。近年来，对话式推荐系统通过使推荐系统能与用户交互，借助一系列澄清问题获取其当前偏好，从而解决了上述局限性。尽管对话式推荐系统已取得进展，现有方案在以下两方面仍远未令人满意：1) 当前对话式推荐系统通常需要用户回答大量澄清问题后才能给出最终推荐，这损害了用户体验；2) 显式提及属性与物品的学习表示之间存在语义鸿沟。为解决这些问题，我们引入知识图谱作为辅助信息来理解与推理用户偏好，并提出了一种新的对话式推荐系统框架——知识增强对话推理系统。由于用户可通过属性级和物品级表达反映其偏好，KECR通过嵌入知识图谱中的结构化知识来弥合两个层级间的语义鸿沟。同时，KECR利用知识图谱内的连通性对用户需求进行显式推理，使模型减少对用户澄清问题反馈的依赖。KECR能够找到突出的推理链，使推荐结果更具可解释性与合理性，并平滑对话过程，从而提升用户体验与对话式推荐准确率。在两个真实数据集上的大量实验表明，我们的方法在自动评估与人工评判中均优于现有最优基线方法。