JUCS - Journal of Universal Computer Science 19(15): 2224-2240, doi: 10.3217/jucs-019-15-2224
A Personalized Recommender System Based on a Hybrid Model
expand article infoWedad Hussein‡, Rasha M. Ismail‡, Tarek F. Gharib§, Mostafa G. M. Mostafa‡
‡ Ain Shams University, Cairo, Egypt§ King Abdulaziz University, Jeddah, Saudi Arabia

Corresponding author: Wedad Hussein ( wedad.hussein@fcis.asu.edu.eg )

© Wedad Hussein, Rasha Ismail, Tarek Gharib, Mostafa G. M. Mostafa.
This article is freely available under the J.UCS Open Content License.
Citation: Hussein W, Ismail RM, Gharib TF, Mostafa MGM (2013) A Personalized Recommender System Based on a Hybrid Model. JUCS - Journal of Universal Computer Science 19(15): 2224-2240. https://doi.org/10.3217/jucs-019-15-2224
Open Access
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
recommender systems, web usage mining, next page prediction