Paper Info
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.
Year
DOI
Venue
2016
10.1109/MDM.2016.57
2016 17th IEEE International Conference on Mobile Data Management (MDM)
Keywords
Field
DocType
Context-aware Recommendation Systems,Location Context,Weather Context,Public Transit,Unsupervised Learning
Recommender system,Weather map,Computer science,Modulo,Mode of transport,Public transport,Unsupervised learning,Web application,Database
Conference
Volume
ISBN
Citations 
1
978-1-5090-0884-1
0
PageRank 
References 
Authors
0.34
4
4
Name
Order
Citations
PageRank
Muhammed Mas-ud Hussain1123.43
Besim Avci2315.46
Goce Trajcevski31732141.26
Peter Scheuermann42606602.83