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首页> 外文期刊>Affective Computing, IEEE Transactions on >Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis
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Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

机译：基于消费者情感和行为分析的个性化本体生成

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摘要

The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.
机译：消费者情绪与其购买行为之间的关系已得到充分证明。精通技术的消费者在承诺购买产品之前，经常会使用网络查找有关产品和服务的信息。我们提出了一种语义Web使用挖掘方法，用于从带注释的Web使用日志中发现定期的Web访问模式，该模式通过自我报告和行为跟踪将有关消费者情绪和行为的信息合并在一起。我们使用模糊逻辑来表示基于周期性模式的Web访问活动的现实时态概念（例如，早晨）和请求的资源属性（请求的URL的本体域概念）。这些包含行为和情感暗示的模糊的时间和资源表示形式，已合并到对用户的Web访问活动进行建模的“个人Web使用格”中。据此，我们生成了以OWL编写的Personal Web Usage Ontology，可启用语义Web应用程序，例如个性化Web资源推荐。最后，我们通过在具有不同程度的情感影响的个性化Web资源推荐的背景下呈现实验结果，证明了我们方法的有效性。情绪影响已被发现有助于个性化推荐中的适应。

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