Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
When recommendation meets mobile: contextual and personalized recommendation on the go
Proceedings of the 13th international conference on Ubiquitous computing
Proceedings of the 13th international conference on Ubiquitous computing
User-dependent aspect model for collaborative activity recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Collaborative boosting for activity classification in microblogs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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With the growing number of smartphones and increasing interest of location-based social network, check-in becomes more and more popular. Check-in means a user has visited a location, e.g., a Point of Interest (POI). The category of the POI implies the activities which can be conducted. In this paper, we are trying to discover the categories of the POIs in which users are being located (i.e., activities) based on GPS reading, time, user identification and other contextual information. However, in the real world, a single user's data is often insufficient for training individual activity recognition model due to limited check-ins each day. Thus we study how to collaboratively use similar users' check-in histories to train Conditional Random Fields (CRF) to provide better activity recognition for each user. We leverage k-Nearest Neighbors (kNN) and Hierarchical Agglomerative Clustering (HAC) for clustering similar users and learn a separated CRF for each cluster on the histories of its users. As for similarity, the first metric involves linear combination of three types of user factors attained by matrix decomposition on User-Activity, User-Temporal and User-Transition matrices. The second metric between two clusters can be the cosine similarity between weights of CRF corresponding to these two clusters. By the initial experiment on real world check-in data from Dianping, we show that it is possible to improve the classifier performance through collaboration and that the first similarity metric is not good to find the real neighbors.