An Efficient and Intelligent Recommender System for Mobile Platform.

Muhammad Jabbar; QAISAR JAVAID; Muhammad Arif; ASIM MUNIR; ALI JAVED

doi:10.22581/muet1982.1804.02

Author(s)

Muhammad Jabbar Department of Computer Science, University of Gujrat, Gujrat, Pakistan

QAISAR JAVAID Department of Computer Systems & Software Engineering, International Islamic University, Islamabad, Pakistan

Muhammad Arif Department of Computer Science, University of Gujrat, Gujrat, Pakistan

ASIM MUNIR Department of Computer Systems & Software Engineering, International Islamic University, Islamabad, Pakistan

ALI JAVED Department of Software Engineering, University of Engineering and Technology (UET), Taxila, Pakistan

Abstract

Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment's, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

Publication Details

Page(s) 463-480

DOI 10.22581/muet1982.1804.02

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 37, Issue: 4, Year: 2018

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