Personalized collaborative filtering recommender system using domain knowledge

In the current era of web applications such as e-retail business, the web services focused to provide personalized search systems to the targeted user intents based on the navigation patterns. Intelligent collaborative filtering recommender system tries to recommend the web pages considering the similar patterns of the other users along with the usage knowledge of the current user session. This recommender systems strategy lacks of the domain knowledge in comparing the usage patterns of the other users in serving with recommendations. This paper mainly focused on incorporating the domain knowledge and usage knowledge in personalization as well as in comparing the similar user patterns for recommender systems. This novel strategy builds a model to recommend the web pages that can help the new search scenarios and can improve the likelihood of a user towards the host website. Experimental results shown that the proposed novel strategy yields to gain in performance of the recommender system in terms of the quality of the web page recommendations.