An Empirical Recommendation Framework to Support Location-Based Services

[thumbnail of futureinternet-12-00154.pdf]
futureinternet-12-00154.pdf - Published Version (2MB) | Preview
Available under license: Creative Commons Attribution

Roy, Animesh Chandra, Arefin, Mohammad Shamsul ORCID: 0000-0003-0259-7624, Kayes, ASM ORCID: 0000-0002-2421-2214, Hammoudeh, Mohammad ORCID: 0000-0003-1058-0996 and Ahmed, Khandakar ORCID: 0000-0003-1043-2029 (2020) An Empirical Recommendation Framework to Support Location-Based Services. Future Internet, 12 (9). p. 154. ISSN 1999-5903


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.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.3390/fi12090154
Official URL
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 0805 Distributed Computing
Current > Division/Research > College of Science and Engineering
Keywords Zaltman metaphor elicitation technique, location-based service, recommendation system, collaborative filtering, fuzzy cognitive map
Citations in Scopus 2 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login