GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Shared Experiences in Personalized Route Planning
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
How a personalized geowiki can help bicyclists share information more effectively
Proceedings of the 2007 international symposium on Wikis
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Programming collective intelligence
Programming collective intelligence
The computational geowiki: what, why, and how
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Aspects of personal navigation with collaborative user feedback
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
A data model for trip planning in multimodal transportation systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A mobile location-based information recommendation system based on GPS and WEB2.0 services
WSEAS Transactions on Computers
Engineering Route Planning Algorithms
Algorithmics of Large and Complex Networks
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
GeoLife2.0: A Location-Based Social Networking Service
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Personalized user interfaces for product configuration
Proceedings of the 15th international conference on Intelligent user interfaces
Biketastic: sensing and mapping for better biking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering recommender systems
The adaptive web
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Geowiki + route analysis = improved transportation planning
Proceedings of the 2013 conference on Computer supported cooperative work companion
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Users have come to rely on automated route finding services for driving, public transit, walking, and bicycling. Current state of the art route finding algorithms typically rely on objective factors like time and distance; they do not consider subjective preferences that also influence route quality. This paper addresses that need. We introduce a new framework for evaluating edge rating prediction techniques in transportation networks and use it to explore ten families of prediction algorithms in Cyclopath, a geographic wiki that provides route finding services for bicyclists. Overall, we find that personalized algorithms predict more accurately than non-personalized ones, and we identify two algorithms with low error and excellent coverage, one of which is simple enough to be implemented in thin clients like web browsers. These results suggest that routing systems can generate better routes by collecting and analyzing users' subjective preferences.