Conversational recommender systems (CRS) enhance the expressivity and personalization of recommendations through multiple turns of user-system interaction. Critiquing is a well-known paradigm for CRS that allows users to iteratively refine recommendations by providing feedback about attributes of recommended items. While existing critiquing methodologies utilize direct attributes of items to address user requests such as 'I prefer Western movies', the opportunity of incorporating richer contextual and side information about items stored in Knowledge Graphs (KG) into the critiquing paradigm has been overlooked. Employing this substantial knowledge together with a well-established reasoning methodology paves the way for critique-based recommenders to allow for complex knowledge-based feedback (e.g., 'I like movies featuring war side effects on veterans') which may arise in natural user-system conversations. In this work, we aim to increase the flexibility of critique-based recommendation by integrating KGs and propose a novel Bayesian inference framework that enables reasoning with relational knowledge-based feedback. We study and formulate the framework considering a Gaussian likelihood and evaluate it on two well-known recommendation datasets with KGs. Our evaluations demonstrate the effectiveness of our framework in leveraging indirect KG-based feedback (i.e., preferred relational properties of items rather than preferred items themselves), often improving personalized recommendations over a one-shot recommender by more than 15%. This work enables a new paradigm for using rich knowledge content and reasoning over indirect evidence as a mechanism for critiquing interactions with CRS.

翻译：对话推荐系统（CRS）通过多轮用户-系统交互增强了推荐的表现力和个性化。批判是一种经典的CRS范式，允许用户通过对推荐项目的属性提供反馈来迭代优化推荐结果。尽管现有批判方法利用项目的直接属性来处理用户请求（例如“我更喜欢西方电影”），但将知识图谱（KG）中存储的丰富上下文信息和项目侧面信息整合到批判范式中的机会却被忽视了。将这一实质性知识与成熟的推理方法相结合，为基于批判的推荐系统开辟了处理自然用户-系统对话中可能出现的复杂知识型反馈（例如“我喜欢展现战争对老兵副作用影响的电影”）的途径。本研究旨在通过整合知识图谱来增强基于批判的推荐的灵活性，并提出了一种新型贝叶斯推理框架，该框架能够实现基于关系知识反馈的推理。我们采用高斯似然函数对该框架进行建模和公式化，并在两个著名的带知识图谱的推荐数据集上进行了评估。评估结果表明，我们的框架在利用基于知识图谱的间接反馈（即用户偏好的项目关系属性而非项目本身）方面表现出色，通常能将单次推荐系统的个性化推荐性能提升15%以上。本研究开创了一种利用丰富知识内容和对间接证据进行推理的新范式，作为与CRS进行批判交互的机制。