Preference based Filtering and Recommendations for Running Routes

Hassan Issa, Amir Guirguis, Shary Beshara, Stefan Agne, Andreas Dengel

Abstract

With the current trend of fitness and health tracking and quantified self, hundreds of relevant apps and devices are being released to the consumer market. Remarkably, some platforms were created to collect running-route data from these different sources in order to provide a better value for users. Such data could be employed in finding running routes based on the user’s preferences rather than being limited to the proximity to the user’s location. In this work, a classification system for running routes is introduced considering performance factors, visual factors and the nature of route. A running-route content-based recommender system is built on top of this classification enabling learning user preferences from their performance history. The system was evaluated using data from active runners and attained a promising recommendation accuracy averaging 84% among all subject users.

References

  1. Barber, C. B., Dobkin, D. P., and Huhdanpaa, H. (1996). The quickhull algorithm for convex hulls. ACM Trans. Math. Softw., 22(4):469-483.
  2. Chen, Y., Bell, M., and Bogenberger, K. (2007). Reliable pretrip multipath planning and dynamic adaptation for a centralized road navigation system. Intelligent Transportation Systems, IEEE Transactions on, 8(1):14-20.
  3. Douglas, D. (1973). Algorithms for the reduction of the number of points required to represent a line or its a caricature. The Canadian Cartographer, 10(2):112- 122.
  4. Hirsch, J. A., James, P., Robinson, J. R. M., Eastman, K. M., Conley, K. D., Evenson, K. R., and Laden, F. (2014). Using mapmyfitness to place physical activity into neighborhood context. Frontiers in Public Health, 2(19).
  5. Issa, H., Shafaee, A., Agne, S., Baumann, S., and Dengel, A. (2015). User-sentiment based evaluation for market fitness trackers - evaluation of fitbit one, jawbone up and nike+ fuelband based on amazon.com customer reviews. In ICT4AgeingWell 2015 - Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and eHealth, Lisbon, Portugal, 20-22 May, 2015., pages 171-179.
  6. Järvelin, K. and Kekäläinen, J. (2000). IR evaluation methods for retrieving highly relevant documents. In SIGIR, pages 41-48.
  7. Knoch, S., Chapko, A., Emrich, A., Werth, D., and Loos, P. (2012). A context-aware running route recommender learning from user histories using artificial neural networks. In Database and Expert Systems Applications (DEXA), 2012 23rd International Workshop on, pages 106-110.
  8. Pang, G., Takahashi, K., Yokota, T., and Takenaga, H. (1995). Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches. In Vehicle Navigation and Information Systems Conference, 1995. Proceedings. In conjunction with the Pacific Rim TransTech Conference. 6th International VNIS. 'A Ride into the Future', pages 75-82.
  9. Quercia, D., Schifanella, R., and Aiello, L. M. (2014). The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. CoRR, abs/1407.1031.
  10. Sasaki, W. and Takama, Y. (2013). Walking route recommender system considering saw criteria. In Technologies and Applications of Artificial Intelligence (TAAI), 2013 Conference on, pages 246-251.
  11. Shafaee, A., Issa, H., Agne, S., Baumann, S., and Dengel, A. (2014). Aspect-based sentiment analysis of amazon reviews for fitness tracking devices. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA,Tainan, Taiwan, May 13-16, 2014. Revised Selected Papers, pages 50-61.
  12. Sinnott (1984). Virtues of the haversine. skytel, 68:158.
  13. Suarez-Alvarez, M. M., Pham, D.-T., Prostov, M. Y., and Prostov, Y. I. (2012). Statistical approach to normalization of feature vectors and clustering of mixed datasets. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 468(2145):2630-2651.
Download


Paper Citation


in Harvard Style

Issa H., Guirguis A., Beshara S., Agne S. and Dengel A. (2016). Preference based Filtering and Recommendations for Running Routes . In Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-186-1, pages 139-146. DOI: 10.5220/0005897801390146


in Bibtex Style

@conference{webist16,
author={Hassan Issa and Amir Guirguis and Shary Beshara and Stefan Agne and Andreas Dengel},
title={Preference based Filtering and Recommendations for Running Routes},
booktitle={Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2016},
pages={139-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005897801390146},
isbn={978-989-758-186-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Preference based Filtering and Recommendations for Running Routes
SN - 978-989-758-186-1
AU - Issa H.
AU - Guirguis A.
AU - Beshara S.
AU - Agne S.
AU - Dengel A.
PY - 2016
SP - 139
EP - 146
DO - 10.5220/0005897801390146