Modern Information Retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Deducing trip related information from flickr
Proceedings of the 18th international conference on World wide web
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Learning travel recommendations from user-generated GPS traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
How random walks can help tourism
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
LARS: A Location-Aware Recommender System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
A trajectory-based recommender system for tourism
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Mining User Mobility Features for Next Place Prediction in Location-Based Services
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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In this paper, we tackle the problem of predicting the "next" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.