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Mining Interesting Locations and Travel Sequences from GPS Trajectories defense by Alok Rakkhit Overview Take GPS data of Multiple users to find travel sequences and interesting locations That data can be used to help understanding of surrounding area and provide travel recommendations Interesting places get many users and experienced users Interest and user experience are region-related Knowledge of Beijing doesn’t imply knowledge of New York Architecture Take raw data and determine locations (stay points) and travel paths Combine users location histories and create TreeBased Hierarchical Graph Apply Hypertext Induced Topic Search to TBHG to infer location interest and user experience Create travel recomendations based on the data Data 107 users tracked from May 2007 to October 2008 Beijing plus 36 other Chinese cities, as well as cities in US, South Korea, and Japan Stay point detection thresholds prevent inclusion of irrelevant locations (stopping at traffic lights, users’ homes and offices) Clustering using density-based algorithm, OPTICS Evaluation 29 subjects, living in Beijing for over 6 years answered evaluation questions about the locations and paths Baselines for locations were rank by count and by frequency Baseline for paths were rank by count, by interests, and by experience Results GeoLife Significance Project GeoLife Advantages of hierarchical system in understanding location data at multiple scales of resolution Integrate social networking into real time location information Apply location/user relationship to improve recommendation systems