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IJMLC 2018 Vol.8(6): 571-576 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.6.747

Recommender System for Global Terrorist Database Based on Deep Learning

Rafah Shihab Alhamdani, Mohammed Najm Abdullah, and Ismael Abdul Sattar

Abstract—Recommender system represents an effective key solution to overcome information overloaded due to huge in volume, multi levels and autonomous of online information. Recently, deep learning gained significant attention through the revolutionary role advanced in many field likes recognition the speech, analyzing images, as well as natural language processing (NLP). Mixing deep learning context mechanisms with recommender system has been gaining momentum because of its wise performances and valid high quality Recommendations. Compared with known traditional recommendation techniques, one of the deep learning main goals is achieve better understanding of customer demands, the characteristics of an items as well as possible interactions between them. The paper aims to provide a general review of most recent research works on deep learning (DL) formed on recommender systems lead to fostering change of recommender system research. Deep learning with recommendation models can be formed a taxonomy levels that attract many researchers in a various fields. Like this paper focus on using recommender system to detect how terrorist are spreading online propaganda using various forms of social media working with global terrorist database.

Index Terms—Deep learning, recommender system, terrorist attacks, social networking.

Rafah Shihab Alhamdani and Ismael Abdul Sattar are with the Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers & Informatics, Baghdad, Iraq (e-mail: ismaelabdul@uomustansiriyah.edu.iq, asmaell_sj@yahoo.com). Mohammed Najm Abdullah is with the Computer Engineering Dept., University of Technology, Baghdad, Iraq.


Cite: Rafah Shihab Alhamdani, Mohammed Najm Abdullah, and Ismael Abdul Sattar, "Recommender System for Global Terrorist Database Based on Deep Learning," International Journal of Machine Learning and Computing vol. 8, no. 6, pp. 571-576, 2018.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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