Survey on Generic Framework That Integrates Semantic Information

Abstract

Abstract: Identification of the current interests of the user based on the short-term navigational patterns instead of explicit user information has proved to be one of the potential sources for prediction of pages which may be of interest to the user. This would help organizations in various analyses such as web site improvement. Various techniques are employed for achieving personalized recommendation. In this research employs web usage mining techniques for determining the interest of “similar” users, technique for classifying and matching an online user based on his browsing interests. A novel approach for prediction of unvisited pages has been employed. The complete process for next page prediction, represented in the architecture broadly consists of two components: offline component and online component. The offline component involves Data Preprocessing, Pattern Discovery and Pattern Analysis. The outcome of the offline component is the derivation of aggregate usage profiles using web usage mining techniques. The online component is responsible for matching the current user’s profile to the aggregate usage profiles. The scope of this work is to match an online user’s navigational activity with the aggregate usage profiles obtained through mining tasks and provides suitable page next page prediction, which may be of interest to the user. The recommendation process is an online phase and consists of two sub-phases: matching profile and recommendation.

Keywords

• Web Usage Mining
• Semantic Web
• Domain Ontology
• Sequential Pattern Mining
• Recommender Systems

How to Cite

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