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Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users' heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner. [ABSTRACT FROM AUTHOR]

Measuring learner cognition based on their problem-solving performance is a joint discipline of cognitive psychology and machine learning. In the case of learner problem-solving, the interaction between learner and knowledge forms a typical type of signed interaction graph. Interaction graphs are a widely used and effective solution to model the relationships between interacting entities. However, most of previous interaction graph methods are inclined to the observed interactions as positive links but they often fail to consider unobserved and negative links, which leads to an insufficiency in capturing the complete cognition/mis-cognition proximity information. To address this limitation, we propose a knowledge graph representation learning method that is based on signed knowledge interaction network (SKIN). We explicitly model the correct/incorrect cognitive performance as the positively + /negatively − signed links in the graph. The model simultaneously measures the nodes' local and global proximity , and then preserves them in the learned knowledge embedding. We architect a pairwise neural network that is based on a tri-sampling strategy and a sign-driven distance measuring objective function. The network generates knowledge representations by maximizing the knowledge distance between oppositely-signed pairs and minimizing the distance between identically-signed pairs. Our experimental results show the learned knowledge embedding demonstrates a desired Euclidean property and can be visualized with clear classification boundaries. We also show it can power downstream tasks such as learner-performance-prediction. The learned embeddings generate promising prediction scores on this task when compared to several methods in network sign prediction and learner-performance-prediction. [ABSTRACT FROM AUTHOR]

Map-matching is a process that aligns location points on a digital map and it is an essential step in location-based services. However, regular map-matching methods cannot archive very high accuracy due to the errors in raw location data and the complexity of road networks. Hence, the final resort for map matching is often through manual annotation, which is human labour intensive. Therefore, we propose iMatching , an interactive system for map-matching which greatly reduces annotation cost and achieves a high accuracy through an active learning approach. Specifically, we model the mapping of a sequence of location points to a road network as a hidden Markov model and automatically generate an initial result. Then, we select error-prone points on the trajectory and guide the annotator to review, and possibly correct, the results. Our evaluation on both real-world and synthetic data demonstrates that iMatching has a better performance comparing with the existing methods. [ABSTRACT FROM AUTHOR]