World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Topic Detection by Clustering Keywords
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Proceedings of the 18th international conference on World wide web
Tour the world: a technical demonstration of a web-scale landmark recognition engine
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Proceedings of the international conference on Multimedia information retrieval
Equip tourists with knowledge mined from travelogues
Proceedings of the 19th international conference on World wide web
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
MM '11 Proceedings of the 19th ACM international conference on Multimedia
WSM2011: third ACM workshop on social media
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Discovering local attractions from geo-tagged photos
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Nontrivial landmark recommendation using geotagged photos
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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This paper presents Near2me, a prototype system implementing a travel recommender concept that generates recommendations that are not only personalized, but also authentic. Exploitation of implicit situational knowledge makes it possible for Near2me to recommend places that are not necessarily touristic or famous, but rather are genuinely representative of place and also match users' personal interests. The system allows users to explore, evaluate, and understand recommendations, control recommendation direction and discover informative supporting material. This functionality makes it possible for users to assess recommendations and confirm their suitability and authentic nature. The recommendation system makes use of user photos from the image sharing community Flickr. We take the position that a social media-based environment incorporating multimedia content items, user-contributed annotations and social network connections is uniquely suited to providing users with authentic, personalized recommendations. First results of a user study allow us to conclude that users are interested in exploring locations, topics, and people from different perspectives and confirm authenticity as a relevance criterion.