Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

• Published in: IEEE transactions on affective computing Vol. 3; no. 2; pp. 152 - 164
• Main Authors: Fong, A. C. M., Baoyao Zhou, Siu Hui, Jie Tang, Guan Hong
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2012
• Subjects: adaptation in mid to long-term interaction; Association rules; behavioral tracking; consumer habits; Context; Emotion and behavior profiling; knowledge discovery; Lattices; Ontologies; ontology generation; personalization; recommender system; Semantic Web; Semantics; weblog mining
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/T-AFFC.2011.22

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.