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King Abdullah University of Science and Technology

1. Alharbi, Basma Mohammed. Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata.

Degree: 2017, King Abdullah University of Science and Technology

In the mobile era, data capturing individuals’ locations have become unprecedentedly available. Data from Location-Based Social Networks is one example of large-scale user-location data. Such data provide a valuable source for understanding patterns governing human mobility, and thus enable a wide range of research. However, mining and utilizing raw user-location data is a challenging task. This is mainly due to the sparsity of data (at the user level), the imbalance of data with power-law users and locations check-ins degree (at the global level), and more importantly the lack of a uniform low-dimensional feature space describing users. Three latent feature models are proposed in this dissertation. Each proposed model takes as an input a collection of user-location check-ins, and outputs a new representation space for users and locations respectively. To avoid invading users privacy, the proposed models are designed to learn from anonymized location data where only IDs - not geophysical positioning or category - of locations are utilized. To enrich the inferred mobility patterns, the proposed models incorporate metadata, often associated with user-location data, into the inference process. In this dissertation, two types of metadata are utilized to enrich the inferred patterns, timestamps and social ties. Time adds context to the inferred patterns, while social ties amplifies incomplete user-location check-ins. The first proposed model incorporates timestamps by learning from collections of users’ locations sharing the same discretized time. The second proposed model also incorporates time into the learning model, yet takes a further step by considering time at different scales (hour of a day, day of a week, month, and so on). This change in modeling time allows for capturing meaningful patterns over different times scales. The last proposed model incorporates social ties into the learning process to compensate for inactive users who contribute a large volume of incomplete user-location check-ins. To assess the quality of the new representation spaces for each model, evaluation is done using an external application, social link prediction, in addition to case studies and analysis of inferred patterns. Each proposed model is compared to baseline models, where results show significant improvements.

In the mobile era, data capturing individuals’ locations have become unprecedentedly available. Data from Location-Based Social Networks is one example of large-scale user-location data. Such data provide a valuable source for understanding patterns governing human mobility, and thus enable a wide range of research. However, mining and utilizing raw user-location data is a challenging task. This is mainly due to the sparsity of data (at the user level), the imbalance of data with power-law users and locations check-ins degree (at the global level), and more importantly the lack of a uniform low-dimensional feature space describing users. Three latent feature models are proposed in this dissertation. Each proposed…

Advisors/Committee Members: Zhang, Xiangliang, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Gao, Xin, Moshkov, Mikhail, Xiong, Hui.

Subjects/Keywords: mobility pattern inference; graphical models; mixed-membership models; Representation learning

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APA (6th Edition):

Alharbi, B. M. (2017). Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/623122

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alharbi, Basma Mohammed. “Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata.” 2017. Thesis, King Abdullah University of Science and Technology. Accessed August 20, 2017. http://hdl.handle.net/10754/623122.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alharbi, Basma Mohammed. “Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata.” 2017. Web. 20 Aug 2017.

Vancouver:

Alharbi BM. Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2017. [cited 2017 Aug 20]. Available from: http://hdl.handle.net/10754/623122.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alharbi BM. Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata. [Thesis]. King Abdullah University of Science and Technology; 2017. Available from: http://hdl.handle.net/10754/623122

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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