A Personalized Recommender System Based on a Hybrid Model
Wedad Hussein (Ain Shams University, Egypt)
Rasha M. Ismail (Ain Shams University, Egypt)
Tarek F. Gharib (King Abdulaziz University, Saudi Arabia)
Mostafa G. M. Mostafa (Ain Shams University, Egypt)
Abstract: Recommender systems are means for web personalization and tailoring the browsing experience to the users' specific needs. There are two categories of recommender systems; memory-based and model-based systems. In this paper we propose a personalized recommender system for the next page prediction that is based on a hybrid model from both categories. The generalized patterns generated by a model based techniques are tailored to specific users by integrating user profiles generated from the traditional memory-based system's user-item matrix. The suggested system offered a significant improvement in prediction speed over traditional model-based usage mining systems, while also offering an average improvement in the system accuracy and system precision by 0.27% and 2.35%, respectively.
Keywords: next page prediction, recommender systems, web usage mining
Categories: I.5.1, I.5.3, I.5.4, L.2.2