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
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]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
