摘要翻译:
我们考虑交互式工具,帮助用户在大量选项集合中搜索他们最喜欢的项目。特别是,我们研究了示例批评,这是一种通过批评呈现给他们的示例选项来使用户能够增量地构造偏好模型的技术。我们提出了改进示例批评技术的新技术,通过在其显示的选项中添加建议。这些建议是基于对用户当前偏好模型和潜在隐藏偏好的分析来计算的。我们用综合用户和真实用户评估了我们基于模型的建议技术的性能。结果表明,这类建议对用户具有很强的吸引力,可以刺激用户表达更多的偏好,从而使用户识别最喜欢的商品的几率提高78%。
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英文标题:
《Preference-based Search using Example-Critiquing with Suggestions》
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作者:
B. Faltings, P. Pu, P. Viappiani
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最新提交年份:
2011
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.
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PDF链接:
https://arxiv.org/pdf/1110.0026