人类响应不确定性下的有序数据挖掘

Sergej Sizov
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引用次数: 2

摘要

对于社会科学、推荐系统研究、市场营销、政治学等许多学科来说,分析和解释有序尺度上的集体反馈是一个重要问题。一个“合理的”模型有望为用户的集体行为提供一个“解释”。许多现有的数据挖掘方法为此目的采用基于某个参数族的分布和混合的概率模型。在现实生活中,用户在做出决定时存在相当大的不确定性。它的评估和使用的概率模型,以更好地解释集体反馈是本文的重点关注。在此过程中,我们介绍了收集个体不确定性的方法,并讨论了它们的可行性和局限性。因此,我们用不确定性知识丰富了最先进的响应挖掘模型(特别是关注潜在用户群体的发现),并在与真实用户的系统实验中展示了由此产生的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining ordinal data under human response uncertainty
Analysis and interpretation of collective feedback on ordinal scales is an important issue for several disciplines, including social sciences, recommender systems research, marketing, political science, and many others. A "reasonable" model is expected to provide an "explanation" of collective user behaviour. Many existing data mining approaches employ for this purpose probabilistic models, based on distributions and mixtures from a certain parametric family. In real life, users meet their decisions with considerable uncertainty. Its assessment and use in probabilistic models for better interpretation of collective feedback is the key concern of this paper. In doing so, we introduce approaches for gathering individual uncertainty, and discuss their viability and limitations. Consequently, we enrich state of the art response mining models (especially focused on discovery of latent user groups) with uncertainty knowledge, and demonstrate resulting advantages in systematic experiments with real users.
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