Users of Location-Based Social Networks (LBSN) are giving away information about their whereabouts, and their interactions in the geographic space. In comparison to other types of personal data, location data are sensitive and can reveal user’s daily routines, activities, experiences and interests in the physical world. As a result, the user is facing an information overload that overburdens him to make a satisfied decision on where to go or what to do in a place. Thus, finding the matching places, users and content is one of the key challenges in LSBNs. This thesis investigates the different dimensions of data collected on LBSNs and proposes a user and place modelling framework. In particular, this thesis proposes a novel approach for the construction of different views of personal user profiles that reflect their interest in geographic places, and how they interact with geographic places. Three novel modelling frameworks are proposed, the static user model, the dynamic user model and the semantic place model. The static user model is a basic model that is used to represent the overall user interactions towards places. On the other hand, the dynamic user model captures the change of the user’s preferences over time. The semantic place model identifies user activities in places and models the relationships between places, users, implicit place types, and implicit activities. The proposed models demonstrate how geographic place characteristics as well as implicit user interactions in the physical space can further enrich the user profiles. The enrichment method proposed is a novel method that combines the semantic and the spatial influences into user profiles. Evaluation of the proposed methods is carried out using realistic data sets collected from the Foursquare LBSN. A new Location and content recommendation methods are designed and implemented to enhance existing location recommendation methods and results showed the usefulness of considering place semantics and the time dimension when the proposed user profiles in recommending locations and content. The thesis considers two further related problems; namely, the construction of dynamic place profiles and computing the similarity between users on LBSN. Dynamic place profiles are representations of geographic places through users’ interaction with the places. In comparison to static place models represented in gazetteers and map databases, these place profiles provide a dynamic view of how the places are used by actual people visiting and interacting with places on the LBSN. The different views of personal user profiles constructed within our framework are used for computing the similarity between users on the LBSN. Temporal user similarities on both the semantic and spatial levels are proposed and evaluated. Results of this work show the challenges and potential of the user data collected on LBSN.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:704961 |
| Date | January 2017 |
| Creators | Mohamed, Soha |
| Publisher | Cardiff University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://orca.cf.ac.uk/98190/ |
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