Title
An Empirical Recommendation Framework To Support Location-Based Services
Abstract
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 theF-measurescore 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.
Year
DOI
Venue
2020
10.3390/fi12090154
FUTURE INTERNET
Keywords
DocType
Volume
location-based services, grid structure, recommendation system, machine learning, clustering, collaborative filtering
Journal
12
Issue
Citations 
PageRank 
9
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Animesh Chandra Roy100.34
Mohammad Shamsul Arefin22613.23
A. S. M. Kayes37815.75
Mohammad Hammoudeh422536.88
Khandakar Ahmed5547.92