Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
A Spatial User Similarity Measure for Geographic Recommender Systems
GeoS '09 Proceedings of the 3rd International Conference on GeoSpatial Semantics
Location-based service with context data for a restaurant recommendation
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
| Hi-index | 0.00 |
In this paper, we present a method for boosting collaborative filtering by integrating spatial information about geo-referenced items (e.g., photos). In particular, we developed a method to estimate missing ratings by propagating an item's neighbor's ratings based on the similarity of geospatial information. An empirical evaluation shows that geospatial information significantly improves recommendation results, and its contribution grows with the ratings data's level of sparseness. We illustrate the usefulness of the method for a photo recommendation task using data obtained from two popular photo-sharing websites: Flickr and Panoramio. A comparison with state-of-the-art methods indicates the superiority of the proposed method, implying that geospatial information should be considered, when available.