Preference elicitation explicitly asks users what kind of recommendations they would like to receive. It is a popular technique for conversational recommender systems to deal with cold-starts. Previous work has studied selection bias in implicit feedback, e.g., clicks, and in some forms of explicit feedback, i.e., ratings on items. Despite the fact that the extreme sparsity of preference elicitation interactions make them severely more prone to selection bias than natural interactions, the effect of selection bias in preference elicitation on the resulting recommendations has not been studied yet. To address this gap, we take a first look at the effects of selection bias in preference elicitation and how they may be further investigated in the future. We find that a big hurdle is the current lack of any publicly available dataset that has preference elicitation interactions. As a solution, we propose a simulation of a topic-based preference elicitation process. The results from our simulation-based experiments indicate (i) that ignoring the effect of selection bias early in preference elicitation can lead to an exacerbation of overrepresentation in subsequent item recommendations, and (ii) that debiasing methods can alleviate this effect, which leads to significant improvements in subsequent item recommendation performance. Our aim is for the proposed simulator and initial results to provide a starting point and motivation for future research into this important but overlooked problem setting.

翻译：偏好获取通过明确询问用户希望获得何种推荐，是对话式推荐系统处理冷启动问题的常用技术。以往研究关注了隐式反馈（如点击）和部分显式反馈（如对物品的评分）中的选择偏差。尽管偏好获取交互的极端稀疏性使其比自然交互更容易受到选择偏差的影响，但偏好获取中的选择偏差对最终推荐结果的影响尚未得到研究。为填补这一空白，我们首次探索了偏好获取中选择偏差的影响，并探讨未来可能的研究方向。研究发现，当前主要障碍是缺乏公开可用的包含偏好获取交互的数据集。为此，我们提出了一种基于主题的偏好获取过程仿真方法。基于仿真的实验结果表明：（i）在偏好获取初期忽略选择偏差的影响会导致后续物品推荐中过度表征问题加剧；（ii）去偏方法可缓解该效应，从而显著提升后续物品推荐性能。我们期望所提出的仿真器与初步研究结果能为这一重要但被忽视的问题场景提供研究起点和动力。