Shreepriya Shreepriya
ABSTRACT
Recommender systems for runners primarily rely on existing running traces in an area. In the absence of running traces, recommending running routes is challenging. This paper describes our approach to generating and proposing ”pleasant” running tours that consider the runner’s standard preferences and their distance and elevation constraints. Our algorithm is an approach to solve the cold start recommendation problem in unknown places by mining available map-data. We implemented a prototypical smartphone app that generates and recommends pleasant running routes to evaluate our algorithm’s effectiveness. An in-the-wild user study was conducted, with 11 participants across three cities. We tested the correlation between what is defined as ”pleasant path” by our algorithm and the user’s perception. The results of the user study show a positive correlation and support our algorithm. We also outline implications for the design of successful recommendation algorithms for runners.
- Gediminas Adomavicius, Nikos Manouselis, and YoungOk Kwon. 2011. Multi-Criteria Recommender Systems. Springer US, Boston, MA.769–803 pages. https://doi.org/10.1007/978-0-387-85820-3_24Google Scholar
Cross Ref
- Syunsuke Bamba. 2012. Tour route generating device, tour route generating method, and program. https://patents.google.com/patent/US20120136566A1/en#patentCitationsGoogle Scholar
- Amanda Brooks. 2006. Route based on distance. https://patents.google.com/patent/US20060206258A1/enGoogle Scholar
- Danilo Gallo, Shreepriya Shreepriya, and Jutta Willamowski. 2020. RunAhead: Exploring Head Scanning based Navigation for Runners. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu, Hawai, 1–13. https://doi.org/10.1145/3313831.3376828Google ScholarDigital Library
- Strava Inc. 2021. Strava. Strava. https://blog.strava.com/routes/Accessed: 2021-02-22.Google Scholar
- Benedikt Loepp and Jürgen Ziegler. 2018. Recommending Running Routes: Framework and Demonstrator. In Proceedings of the 2nd Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18). RecSys, Vancouver, 26–29. http://toinebogers.com/workshops/complexrec2018/resources/proceedings.pdf#page=26Google Scholar
- Garmin Ltd.2021. Creating a Course in Garmin Connect | Garmin Support. Garmin. https://support.garmin.com/en-US/?faq=tWx3JQyjlU474jqa91EJs8Accessed: 2021-02-22.Google Scholar
- David K. McGookin and Stephen A. Brewster. 2013. Investigating and supporting undirected navigation for runners. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems on - CHI EA ’13. ACM Press, Paris, France, 1395. https://doi.org/10.1145/2468356.2468605Google ScholarDigital Library
- Damien Mercier. 2016. PleasantTourFinder : an application to find the most pleasant tour from a given location. https://core.ac.uk/display/161329299Google Scholar
- Sofia Michel, Stephane Clinchant, Christophe Legras, and Jutta Willamowski. 2020. System and method for generating pedestrian tours. https://patents.google.com/patent/US20200309545A1/en/Google Scholar
- World Health Organisation. 2021. Coronavirus disease (COVID-19) – World Health Organization. World Health Organisation. https://www.who.int/emergencies/diseases/novel-coronavirus-2019Accessed: 2021-02-22.Google Scholar
- Mathew Schwartz. 2020. Evaluating Jogging Routes in Mass Models. In SimAUD ’20: Simulation in Architecture and Urban Design. ACM, Virtual Event, 00–00.Google Scholar
- Shreepriya Shreepriya, Jutta Willamowski, and Danilo Gallo. 2019. Supporting Natural Navigation for Running in Unknown Places. In Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion (San Diego, CA, USA) (DIS ’19 Companion). Association for Computing Machinery, New York, NY, USA, 277–281. https://doi.org/10.1145/3301019.3323895Google ScholarDigital Library
- Iwan Van Hende and Tao-Yuan Hsien. 2012. US 20130304377A1 - Method of creating varied exercise routes for a user | RPX Insight. https://insight.rpxcorp.com/patent/US20130304377A1Google Scholar
- Jutta Willamowski, Stephane Clinchant, Christophe Legras, Sofia Michel, and Shreepriya Shreepriya. 2019. Running Tour Generation for Unknown Environments. In HCI International 2019 - Posters, Constantine Stephanidis (Ed.). Vol. 1033. Springer International Publishing, Cham, 528–535. https://doi.org/10.1007/978-3-030-23528-4_72 Series Title: Communications in Computer and Information Science.Google ScholarCross Ref
Index Terms
- Evaluating an Itinerary Recommendation Algorithm for Runners
Recommendations
Social itinerary recommendation from user-generated digital trails
Planning travel to unfamiliar regions is a difficult task for novice travelers. The burden can be eased if the resident of the area offers to help. In this paper, we propose a social itinerary recommendation by learning from multiple user-generated ...
Exploiting semantics for context-aware itinerary recommendation
Itinerary planning is a challenging task for users wishing to enjoy points of interest (POIs) in line with their preferences, the current context of use, and travel constraints. This article describes an approach to exploit linked open data (LOD) to ...
Personalized Itinerary Recommendation with Queuing Time Awareness
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalPersonalized itinerary recommendation is a complex and time-consuming problem, due to the need to recommend popular attractions that are aligned to the interest preferences of a tourist, and to plan these attraction visits as an itinerary that has to be ...
Comments