Parallel algorithm based consumer behavior analysis for generating personalized ontology system

Consumer face a dramatic problem related to web search. They expect the most relevant and efficient results. While the result often disappoint the consumer and also their precious time. This paper propose a semantic web usage mining approach for discovering periodic internet access patterns from elucidated net usage logs which incorporates data on client emotions and behaviors through self-reporting and behavior tracking. We use fuzzy logic to represent real-life temporal ideas (e.g., morning) and requested resource attributes (ontological domain ideas for the requested URLs) of periodic pattern-primarily based web access activities. These fuzzy temporal and resource representations, that contain both behavioral and emotional cues, are incorporated into a private Web Usage Lattice that models the user's web access activities. From this, we tend to generate a private Net Usage Ontology, which enables semantic web applications such as customized web resources recommendation. Finally, we have a tendency to demonstrate the effectiveness of our approach by presenting experimental leads to the context of personalized net resources recommendation with varying degrees of emotional influence.