Gaining insights into the preferences of new users and subsequently personalizing recommendations necessitate managing user interactions intelligently, namely, posing pertinent questions to elicit valuable information effectively. In this study, our focus is on a specific scenario of the cold-start problem, where the recommendation system lacks adequate user presence or access to other users' data is restricted, obstructing employing user profiling methods utilizing existing data in the system. We employ Active Learning (AL) to solve the addressed problem with the objective of maximizing information acquisition with minimal user effort. AL operates for selecting informative data from a large unlabeled set to inquire an oracle to label them and eventually updating a machine learning (ML) model. We operate AL in an integrated process of unsupervised, semi-supervised, and supervised ML within an explanatory preference elicitation process. It harvests user feedback (given for the system's explanations on the presented items) over informative samples to update an underlying ML model estimating user preferences. The designed user interaction facilitates personalizing the system by incorporating user feedback into the ML model and also enhances user trust by refining the system's explanations on recommendations. We implement the proposed preference elicitation methodology for food recommendation. We conducted human experiments to assess its efficacy in the short term and also experimented with several AL strategies over synthetic user profiles that we created for two food datasets, aiming for long-term performance analysis. The experimental results demonstrate the efficiency of the proposed preference elicitation with limited user-labeled data while also enhancing user trust through accurate explanations.

翻译：洞察新用户的偏好并随后个性化推荐，需要智能地管理用户交互，即提出相关问题以有效获取有价值的信息。本研究聚焦于冷启动问题的一个特定场景，其中推荐系统缺乏足够的用户数据或对其他用户数据的访问受限，从而阻碍了利用系统中现有数据的方法进行用户画像构建。我们采用主动学习来解决这一问题，目标是以最小的用户努力最大化信息获取。主动学习用于从大量未标记数据集中选择信息量大的样本，以询问专家（或用户）为其标记，并最终更新机器学习模型。我们将主动学习应用于一个集成无监督、半监督和监督机器学习的过程，该过程嵌入于可解释的偏好获取流程中。它收集用户对系统展示项目解释的反馈，并基于这些信息量大的样本更新用于估计用户偏好的底层机器学习模型。设计的用户交互通过将用户反馈融入机器学习模型，有助于个性化系统，同时通过优化系统对推荐结果的解释来增强用户信任。我们实现了该偏好获取方法用于食物推荐。我们进行了人类实验以评估其短期效果，并针对两个食物数据集创建了合成用户画像，测试了几种主动学习策略，旨在进行长期性能分析。实验结果表明，所提出的偏好获取方法在有限的用户标记数据下表现高效，同时通过准确的解释增强了用户信任。