Personalized Web Page Recommender System using integrated Usage and Content Knowledge

Now a day, intelligent recommender systems on the web intends to recommend web pages for individual users by discovering useful knowledge from Web usage data and web content data. Knowledge representation for the web contents and integrating with web usage knowledge are the challenging issues to make Web page recommendations effective. This paper presents an effective method to integrate the domain knowledge and web usage knowledge of a website through semantics. Perhaps, a new model is framed to construct a semantic hierarchy of the web log data and the domain contents, which represents the integrated usage knowledge and domain knowledge. This model has two phases: first one is to generate domain knowledge represented with ontology for the website; second one is to generate mappings between web pages and domain terms in the ontology based on the usage of an individual. However this semantically enhanced knowledge representation uses a recommendation strategy to recommend web pages dynamically. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining method. Finally, explores the effectiveness of the proposed approach than the existing Web Usage Mining by the analysis of the experiments within the scope of web page recommendations.