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Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
Department of Computer Science, Faculty of Computer Science and Information Technology, Bayero University, Kano, 3011 Kano, Nigeria
Department of Tourism and Hospitality, Faculty of Tourism, Sekolah Tinggi Pariwisata Ambarrukmo, Yogyakarta 55281, Indonesia
Department of Sociology Education, Faculty of Social Sciences, Universitas Negeri Yogyakrta, Yogyakarta 55281, Indonesia
Department of Computer Science, Gombe School of Science, Federal College of Education (Technical), 0060 Gombe, Nigeria
Department of Fundamental and Applied Research, AMCS Research Center, 55581 Yogyakarta, Indonesia
Department of Information System, Faculty of Information Technology and Electrical Engineering, Universitas Teknologi Yogyakarta, Kampus Jombor, Yogyakarta 55122, Indonesia
Author to whom correspondence should be addressed.
Appl. Sci. 2017, 7(12), 1211;
Received: 24 September 2017 / Revised: 23 October 2017 / Accepted: 13 November 2017 / Published: 5 December 2017
Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users’ recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted. View Full-Text
Keywords: context-aware; extraction; evaluation; filtering; modeling; recommendation context-aware; extraction; evaluation; filtering; modeling; recommendation
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Haruna, K.; Akmar Ismail, M.; Suhendroyono, S.; Damiasih, D.; Pierewan, A.C.; Chiroma, H.; Herawan, T. Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction. Appl. Sci. 2017, 7, 1211.

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