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EMD-Based Semantic User Similarity Using Past Travel Histories

EMD-Based Semantic User Similarity Using Past Travel Histories

Sunita Tiwari, Saroj Kaushik
Copyright: © 2022 |Volume: 24 |Issue: 3 |Pages: 17
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa2
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MLA

Tiwari, Sunita, and Saroj Kaushik. "EMD-Based Semantic User Similarity Using Past Travel Histories." JCIT vol.24, no.3 2022: pp.1-17. http://doi.org/10.4018/JCIT.20220701.oa2

APA

Tiwari, S. & Kaushik, S. (2022). EMD-Based Semantic User Similarity Using Past Travel Histories. Journal of Cases on Information Technology (JCIT), 24(3), 1-17. http://doi.org/10.4018/JCIT.20220701.oa2

Chicago

Tiwari, Sunita, and Saroj Kaushik. "EMD-Based Semantic User Similarity Using Past Travel Histories," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-17. http://doi.org/10.4018/JCIT.20220701.oa2

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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%.