Identifying significant location categories visited by mobile users is the key to a variety of applications. This is an extremely challenging task due to the possible deviation between the estimated location coordinate and the actual location, which could be on the order of kilometers. To estimate the actual location category more precisely, we propose a novel tensor factorization framework, through several key observations including the intrinsic correlations between users, to infer the most likely location categories within the location uncertainty circle. In addition, the proposed algorithm can also predict where users are even in the absence of location information. In order to efficiently solve the proposed framework, we propose a parameter-free and scalable optimization algorithm by effectively exploring the sparse and low-rank structure of the tensor. Our empirical studies show that the proposed algorithm is both efficient and effective: it can solve problems with millions of users and billions of location updates, and also provides superior prediction accuracies on real-world location updates and check-in data sets.
There is a growing interest in leveraging geo-spatial data to provide location-aware services. With a large amount of collected geo-spatial data, a crucial step is to identify important "base" locations (e.g., home or work) and understand users' behavior at these locations. In this paper, we propose an unsupervised collaborative learning approach to identifying home and work locations of individuals from geo-spatial trajectory data. Our approach transforms user trajectory records into intuitive and insightful user-location signatures, clusters these signatures, and then identifies location types based on cluster characteristics. This clustering model can be used to identify base locations for new users. We validate this approach using Open Street Map and Foursquare location tags and obtain an accuracy of 80 . The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual's general life style and regularity. Concretely, we propose the life pattern normal form (the LP-normal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework. The discovery of a person's personally important places involves obtaining the physical locations for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries awl obtain, information in reference, to places, e.g., ''home", ''work" or ''Northwest Health Club". It is a challenge to map from physical locations to jxtrsonally meaningful places because GPS tracks are continuous data both spatially and temporally, while most existing data mining techniques expect discrete data. Previous work has explored algorithms to discover personal places from location data. However, they all have limitations. Our work proposes a two-step approach that discretized continuous GPS data into places and learns important places from the place features. Our approach was validated using real user data and shown to have good accuracy when applied in predicting not only important and frequent places, but also important and not so frequent places.  In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user's GPS trajectories with a semantic location history (SLH), e.g., shopping malls → restaurants → cinemas. Then, we measure the similarity between different users' SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user's interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. As a result, SLH-MTM outperforms the related works [4].