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Article

An Empirical Recommendation Framework to Support Location-Based Services

1
Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh
2
Department of Computer Science and Information Technology, La Trobe University, Victoria 3086, Australia
3
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
4
College of Engineering and Science, Victoria University, Victoria 3011, Australia
*
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in the International Conference on Internet of Things and Connected Technologies (ICIoTCT) 2020 (In Press).
Future Internet 2020, 12(9), 154; https://doi.org/10.3390/fi12090154
Received: 25 August 2020 / Revised: 15 September 2020 / Accepted: 15 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Sustainable Smart City)
The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users. View Full-Text
Keywords: location-based services; grid structure; recommendation system; machine learning; clustering; collaborative filtering location-based services; grid structure; recommendation system; machine learning; clustering; collaborative filtering
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MDPI and ACS Style

Roy, A.C.; Arefin, M.S.; Kayes, A.S.M.; Hammoudeh, M.; Ahmed, K. An Empirical Recommendation Framework to Support Location-Based Services. Future Internet 2020, 12, 154. https://doi.org/10.3390/fi12090154

AMA Style

Roy AC, Arefin MS, Kayes ASM, Hammoudeh M, Ahmed K. An Empirical Recommendation Framework to Support Location-Based Services. Future Internet. 2020; 12(9):154. https://doi.org/10.3390/fi12090154

Chicago/Turabian Style

Roy, Animesh C., Mohammad S. Arefin, A. S.M. Kayes, Mohammad Hammoudeh, and Khandakar Ahmed. 2020. "An Empirical Recommendation Framework to Support Location-Based Services" Future Internet 12, no. 9: 154. https://doi.org/10.3390/fi12090154

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