A Spatial User Similarity Measure for Geographic Recommender Systems

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.

[1]  Samira Si-Said Cherfi,et al.  Advances in Conceptual Modeling - Foundations and Applications , 2008 .

[2]  Steven M. Seitz,et al.  Scene Segmentation Using the Wisdom of Crowds , 2008, ECCV.

[3]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[4]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[5]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.

[6]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[7]  GeunSik Jo,et al.  Location-Based Service with Context Data for a Restaurant Recommendation , 2006, DEXA.

[8]  Marieke Guy,et al.  Folksonomies: Tidying Up Tags? , 2006, D Lib Mag..

[9]  Mor Naaman,et al.  World explorer: visualizing aggregate data from unstructured text in geo-referenced collections , 2007, JCDL '07.

[10]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[11]  Angela Schwering,et al.  Semantic Similarity Measurement and Geospatial Applications , 2008, Trans. GIS.

[12]  Christoph Schlieder,et al.  Modeling Collaborative Semantics with a Geographic Recommender , 2007, ER Workshops.

[13]  Stephan Winter,et al.  Spatial Information Theory, 8th International Conference, COSIT 2007, Melbourne, Australia, September 19-23, 2007, Proceedings , 2007, COSIT.

[14]  Arno Scharl,et al.  The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society , 2007, The Geospatial Web.

[15]  Harith Alani,et al.  Geographical Information Retrieval with Ontologies of Place , 2001, COSIT.

[16]  Christoph Schlieder,et al.  Photographing a City: An Analysis of Place Concepts Based on Spatial Choices , 2009, Spatial Cogn. Comput..

[17]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[18]  Eleanor Rosch,et al.  Principles of Categorization , 1978 .

[19]  Pearl Pu,et al.  A recursive prediction algorithm for collaborative filtering recommender systems , 2007, RecSys '07.

[20]  Lars Schmidt-Thieme,et al.  Taxonomy-driven computation of product recommendations , 2004, CIKM '04.

[21]  Alexander Tuzhilin,et al.  The long tail of recommender systems and how to leverage it , 2008, RecSys '08.

[22]  Mi Zhang,et al.  Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.

[23]  Chris Anderson,et al.  The Long Tail: Why the Future of Business is Selling Less of More , 2006 .

[24]  Wenfei Fan,et al.  Keys with Upward Wildcards for XML , 2001, DEXA.

[25]  Nitya Narasimhan,et al.  Using location for personalized POI recommendations in mobile environments , 2006, International Symposium on Applications and the Internet (SAINT'06).

[26]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[27]  Michael F. Goodchild,et al.  Foundations of Geographic Information Science , 2003 .

[28]  Angela Schwering,et al.  Approaches to Semantic Similarity Measurement for Geo‐Spatial Data: A Survey , 2008, Trans. GIS.

[29]  Max J. Egenhofer,et al.  Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure , 2004, Int. J. Geogr. Inf. Sci..

[30]  Mor Naaman,et al.  Methods for extracting place semantics from Flickr tags , 2009, TWEB.

[31]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[32]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[33]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[34]  A. Tversky.  Features of Similarity , 1977 .