Activity-based and Behavior-based Location Recommendation in Location Based Social Networks

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atmire.migration.oldid1922
dc.contributor.advisorWang, Xin
dc.contributor.authorRahimi, Seyyed Mohammadreza
dc.date.accessioned2014-01-31T18:49:38Z
dc.date.available2016-02-11T21:13:15Z
dc.date.issued2014-01-31
dc.date.submitted2014en
dc.description.abstractLocation-Based Social Networks (LBSNs) are social networks with functionalities that let users share their location information with other users. Location recommendation is the task of suggesting unvisited locations to the users. A good location recommender should make user-specific recommendations based on users’ preferences, geographical constraints and time. In this thesis we investigate the development of two novel location recommendation methods for Location-Based Social Networks (LBSNs), the Probabilistic Category-based Location Recommender (PCLR) and the Behavior-based Location Recommender (BLR). The PCLR method finds the temporal and spatial patterns of users’ activities in the form of temporal and spatial probability distributions. It then uses the patterns to select the right category of locations and recommend nearby locations of that type to the user. On the other hand, the BLR method first extracts user behaviors from their check-in history. It then utilizes a collaborative filtering technique to extract common behaviors and predict behavior of the user at a given time. Finally, BLR filters locations in the user’s proximity based on the predicted behavior when making the location recommendation. PCLR and BLR methods go through a set of experiments on a real-world check-in dataset. These experiments show that PCLR and BLR methods improve the performance of the existing location recommenders in terms of precision and recall. Additionally, the BLR method produces much better recommendations for the cold-start users.en_US
dc.description.embargoterms2 yearsen_US
dc.identifier.citationRahimi, S. M. (2014). Activity-based and Behavior-based Location Recommendation in Location Based Social Networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24693en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/24693
dc.identifier.urihttp://hdl.handle.net/11023/1357
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectArtificial Intelligence
dc.subject.classificationLocation Recommendationen_US
dc.subject.classificationRecommendation Systemsen_US
dc.subject.classificationCold-start problemen_US
dc.titleActivity-based and Behavior-based Location Recommendation in Location Based Social Networks
dc.typemaster thesis
thesis.degree.disciplineGeomatics Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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