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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.
LNSE 2013 Vol.1(3): 303-307 ISSN: 2301-3559
DOI: 10.7763/LNSE.2013.V1.66

Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System

Laila Safoury and Akram Salah
Abstract—Recommender systems have been used tremendously academically and commercially, recommendations generated by these systems aim to offer relevant interesting items to users. Several approaches have been suggested for providing users with recommendations using their rating history, most of these approaches suffer from new user problem (cold-start) which is the initial lack of items ratings. In this paper we suggest utilizing new user demographic data to provide recommendations instead of using rating history to avoid cold-start problem. We present a framework for evaluating the usage of different demographic attributes, such as age, gender, and occupation, for recommendation generation. Experiments are executed using MovieLens dataset to evaluate the performance of the proposed framework.

Index Terms—Demographic filtering, information retrieval, personalization, recommender system.

The authors are with the Computer Science Department, FCI, Cairo University, Cairo, Egypt (e-mail: laila.moustafa@fci-cu.edu.eg, akram.salah@fci-cu.edu.eg).


Cite: Laila Safoury and Akram Salah, "Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System," Lecture Notes on Software Engineering vol. 1, no. 3, pp. 303-307, 2013.

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