Incorporating weather updates for public transportation users of recommendation systems

Muhammed Mas Ud Hussain; Besim Avci; Goce Trajcevski; Peter Scheuermann

doi:10.1109/MDM.2016.57

Incorporating weather updates for public transportation users of recommendation systems

Muhammed Mas Ud Hussain^*, Besim Avci, Goce Trajcevski, Peter Scheuermann

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

This work presents a system for augmenting the functionality of Yelp-like recommendation sites by enabling users to search for places bounded by travel-time when using public transportation, and modifying recommendations based on updated weather conditions. Using public transport, although is cheaper and efficient, entails that only fixed places of boarding/exiting may be used which, in turn, implies walking to (from) a particular location from (to) a given station. Given the impact of the weather on the mood and activities, preferences for a certain type of services may need to be dynamically adjusted based on the current weather or the near-future forecast, modulo travel-routes to preferred locations. In this work, we develop a model to predict a user's preferred mode of transport (car, or public transit) from their old check-ins and incorporate the weather context into the recommendation process. We use event-based modeling to control the extent of walking depending on user-defined tolerance information and live weather conditions. We implemented a web application (both desktop and mobile platforms), utilizing existing tools such as Google Maps Direction API and Open Weather Map API for retrieving real-time information.

Original language	English (US)
Title of host publication	Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016
Editors	Prem Jayaraman, Wei Wu, Chi-Yin Chow
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	333-336
Number of pages	4
ISBN (Electronic)	9781509008834
DOIs	https://doi.org/10.1109/MDM.2016.57
State	Published - Jul 20 2016
Event	17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016 - Porto, Portugal Duration: Jun 13 2016 → Jun 16 2016

Publication series

Name	Proceedings - IEEE International Conference on Mobile Data Management
Volume	2016-July
ISSN (Print)	1551-6245

Other

Other	17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016
Country/Territory	Portugal
City	Porto
Period	6/13/16 → 6/16/16

Keywords

Context-aware Recommendation Systems
Location Context
Public Transit
Unsupervised Learning
Weather Context

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/MDM.2016.57

Cite this

Hussain, M. M. U., Avci, B., Trajcevski, G., & Scheuermann, P. (2016). Incorporating weather updates for public transportation users of recommendation systems. In P. Jayaraman, W. Wu, & C-Y. Chow (Eds.), Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016 (pp. 333-336). Article 7517814 (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MDM.2016.57

Hussain, Muhammed Mas Ud ; Avci, Besim ; Trajcevski, Goce et al. / Incorporating weather updates for public transportation users of recommendation systems. Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016. editor / Prem Jayaraman ; Wei Wu ; Chi-Yin Chow. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 333-336 (Proceedings - IEEE International Conference on Mobile Data Management).

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title = "Incorporating weather updates for public transportation users of recommendation systems",

abstract = "This work presents a system for augmenting the functionality of Yelp-like recommendation sites by enabling users to search for places bounded by travel-time when using public transportation, and modifying recommendations based on updated weather conditions. Using public transport, although is cheaper and efficient, entails that only fixed places of boarding/exiting may be used which, in turn, implies walking to (from) a particular location from (to) a given station. Given the impact of the weather on the mood and activities, preferences for a certain type of services may need to be dynamically adjusted based on the current weather or the near-future forecast, modulo travel-routes to preferred locations. In this work, we develop a model to predict a user's preferred mode of transport (car, or public transit) from their old check-ins and incorporate the weather context into the recommendation process. We use event-based modeling to control the extent of walking depending on user-defined tolerance information and live weather conditions. We implemented a web application (both desktop and mobile platforms), utilizing existing tools such as Google Maps Direction API and Open Weather Map API for retrieving real-time information.",

keywords = "Context-aware Recommendation Systems, Location Context, Public Transit, Unsupervised Learning, Weather Context",

author = "Hussain, {Muhammed Mas Ud} and Besim Avci and Goce Trajcevski and Peter Scheuermann",

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Hussain, MMU, Avci, B, Trajcevski, G & Scheuermann, P 2016, Incorporating weather updates for public transportation users of recommendation systems. in P Jayaraman, W Wu & C-Y Chow (eds), Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016., 7517814, Proceedings - IEEE International Conference on Mobile Data Management, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 333-336, 17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016, Porto, Portugal, 6/13/16. https://doi.org/10.1109/MDM.2016.57

Incorporating weather updates for public transportation users of recommendation systems. / Hussain, Muhammed Mas Ud; Avci, Besim; Trajcevski, Goce et al.
Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016. ed. / Prem Jayaraman; Wei Wu; Chi-Yin Chow. Institute of Electrical and Electronics Engineers Inc., 2016. p. 333-336 7517814 (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - This work presents a system for augmenting the functionality of Yelp-like recommendation sites by enabling users to search for places bounded by travel-time when using public transportation, and modifying recommendations based on updated weather conditions. Using public transport, although is cheaper and efficient, entails that only fixed places of boarding/exiting may be used which, in turn, implies walking to (from) a particular location from (to) a given station. Given the impact of the weather on the mood and activities, preferences for a certain type of services may need to be dynamically adjusted based on the current weather or the near-future forecast, modulo travel-routes to preferred locations. In this work, we develop a model to predict a user's preferred mode of transport (car, or public transit) from their old check-ins and incorporate the weather context into the recommendation process. We use event-based modeling to control the extent of walking depending on user-defined tolerance information and live weather conditions. We implemented a web application (both desktop and mobile platforms), utilizing existing tools such as Google Maps Direction API and Open Weather Map API for retrieving real-time information.

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ER -

Hussain MMU, Avci B, Trajcevski G, Scheuermann P. Incorporating weather updates for public transportation users of recommendation systems. In Jayaraman P, Wu W, Chow C-Y, editors, Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 333-336. 7517814. (Proceedings - IEEE International Conference on Mobile Data Management). doi: 10.1109/MDM.2016.57

Incorporating weather updates for public transportation users of recommendation systems

Abstract

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Keywords

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