Abstract
The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.
Highlights
Handheld devices are the main platforms for communication as well as for information access in the current era
Due to the lack of actual similarity values, the cosine similarity values are considered as base results for comparisons as it is one of the most popular and commonly used similarity measures found in recommender systems
The metrics used for evaluation are the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE)
Summary
Handheld devices are the main platforms for communication as well as for information access in the current era. The handheld devices nowadays are mostly equipped with GPS systems that make it suitable to log users’ travel histories in the form of GPS trajectories. These geo-spatial data have opened various opportunities to retrieve highly relevant information, which are useful in multiple location-based applications. The GPS trajectories are found to be an important source of information about the user (Zheng & Zhou, 2011) This extracted information can infer the movement pattern of user, retrieve the current context of user, identify the habits and likings of the user, and so on and so forth. Analysis of the trajectory data finds application in traffic planning, itinerary planning, advertising, disaster management, tourist spot recommendations, etc
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.
