alexa A New Profile Learning Model for Recommendation System
ISSN: 2165- 7866

Journal of Information Technology & Software Engineering
Open Access

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Research Article

A New Profile Learning Model for Recommendation System based on Machine Learning Technique

Shereen H Ali*, Ali I El Desouky and Ahmed I Saleh

Department of Computer Eng. & Systems, Faculty of Engineering, Mansoura University, Egypt

*Corresponding Author:
Shereen H Ali
Department of Computer Engineering and Systems
Faculty of Engineering, Mansoura University, Egypt
Tel: +20 50 2383781
E-mail: [email protected]

Received Date: Janaury 13, 2016; Accepted Date: February 05, 2016; Published Date: February 21, 2016

Citation: Ali SH, Desouky AIE, Saleh AI (2016) A New Profile Learning Model for Recommendation System based on Machine Learning Technique. J Inform Tech Softw Eng 6:170. doi:10.4172/2165-7866.1000170

Copyright: © 2016 Ali SH. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Recommender systems (RSs) have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent. Adaptive Vertical Recommendation (IAVR) system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy

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