GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Geographical Information Retrieval with Ontologies of Place
COSIT 2001 Proceedings of the International Conference on Spatial Information Theory: Foundations of Geographic Information Science
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society (Advanced Information and Knowledge Processing)
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Scene Segmentation Using the Wisdom of Crowds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Modeling collaborative semantics with a geographic recommender
ER'07 Proceedings of the 2007 conference on Advances in conceptual modeling: foundations and applications
Location-based service with context data for a restaurant recommendation
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
A folksonomy-based recommendation system for the sensor web
W2GIS'11 Proceedings of the 10th international conference on Web and wireless geographical information systems
Improving location recommendations with temporal pattern extraction
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Knowledge-Based Systems
Using geospatial metadata to boost collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
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Recommender systems solve an information filtering task. They suggest data objects that seem likely to be relevant to the user based upon previous choices that this user has made. A geographic recommender system recommends items from a library of georeferenced objects such as photographs of touristic sites. A widely-used approach to recommending consists in suggesting the most popular items within the user community. However, these approaches are not able to handle individual differences between users. We ask how to identify less popular geographic objects that are nevertheless of interest to a specific user. Our approach is based on user-based collaborative filtering in conjunction with an prototypical model of geographic places (heatmaps). We discuss four different measures of similarity between users that take into account the spatial semantic derived from the spatial behavior of a user community. We illustrate the method with a real-world use case: recommendations of georeferenced photographs from the public website Panoramio. The evaluation shows that our approach achieves a better recall and precision for the first ten items than recommendations based on the most popular geographic items.