Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Alimohammadi Alimohammadi,Khazaei Khazaei

doi:10.3390/ijgi8090406

Abstract

Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.

Highlights

Recent developments in mobile communication and location-acquisition technologies have motivated mobile users to share information about their location [1]
The experimental data used in this study was gathered from a popular location-based social networks (LBSNs), Gowalla
The results of evaluation with test data show that the random walk with restart (RWR) algorithm outperforms these approaches

Summary

Introduction

Recent developments in mobile communication and location-acquisition technologies have motivated mobile users to share information about their location [1]. Online content is increasingly enhanced by geographic information, which represents a new context layer and is used for organizing and displaying data. These developments have led to convergence of GIS (Geographic Information System) and social media, resulting in augmentation of the existing social network sites with new location-based capabilities; e.g., Facebook or Twitter, and the development of new ones exclusively around the location-based data, such as Foursquare [2]. User preferences are likely to change in different contexts such as time, location, surrounding people, emotion, devices, weather, etc. Ignoring these contextual variables would lead to a reduction in the efficacy of recommendations. The crucial impact of contextual information on user preferences has led to the development of Context-Aware Recommender Systems (CARSs), which produce more relevant recommendations by considering the particular contextual situation of the user [6]

Objectives

Results

Conclusion