标题： 可解释的主动学习用于偏好获取 显示英文标题

标题： Explainable Active Learning for Preference Elicitation

摘要： 深入了解新用户的偏好并随后进行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地获取有价值的信息。 在本研究中，我们的重点是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存在或无法访问其他用户的数据，阻碍了利用系统中现有数据的用户画像方法。 我们采用主动学习（AL）来解决所提出的问题，目标是以最小的用户努力最大化信息获取。 AL通过从大量未标记的数据集中选择有信息量的数据，向一个Oracle查询以标记它们，并最终更新机器学习（ML）模型。 我们在解释性偏好获取过程中，将无监督、半监督和监督ML集成在一起进行AL操作。 它收集用户反馈（针对系统对展示项目的解释），通过对有信息量的样本进行更新，以改进估计用户偏好的底层ML模型。 设计的用户交互通过将用户反馈纳入ML模型，促进了系统的个性化，同时也通过优化系统对推荐的解释来增强用户信任。 我们实施了所提出的偏好获取方法用于食品推荐。 我们进行了人工实验以评估其短期效果，并且还对为两个食品数据集创建的合成用户档案进行了多种AL策略的实验，旨在进行长期性能分析。 实验结果表明，在有限的用户标记数据下，所提出的偏好获取方法是有效的，同时通过准确的解释增强了用户信任。 显示英文摘要

摘要： 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.