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dc.contributor.advisorWang, Xin
dc.contributor.authorZhou, Dequan
dc.date.accessioned2014-09-23T20:51:29Z
dc.date.available2014-11-17T08:00:46Z
dc.date.issued2014-09-23
dc.date.submitted2014en
dc.identifier.citationZhou, D. (2014). Location Recommendation on Location Based Social Networks Utilizing Check-in Data and Location Category (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26780en_US
dc.identifier.urihttp://hdl.handle.net/11023/1776
dc.description.abstractLocation-based social networks (LBSNs) provide a platform for users to share their location information with each other. Location recommendation is the task of suggesting unvisited locations to the users. It aims to make satisfying recommendations of locations by utilizing the information such as users' visiting histories, user profiles and location profiles. This thesis investigates the utilization of check-in data and location category information for location recommendation on LBSNs. A distributed crawler is developed to collect a large amount of check-in data from Gowalla for the research. Then, three ways are used to utilize the check-in data, namely, binary utilization, FIF utilization, and probability utilization. According to different utilizations, different Collaborative Filtering recommenders are introduced to do location recommendation. Experiments are conducted to compare the performances of different recommenders using different check-in utilizations. Location category information is utilized for location recommendation by considering the temporal and spatial patterns. A user's periodic check-in behaviors at different location categories are represented as temporal curves. A temporal influence model is used to predict similar users' check-in behaviors based on temporal curves. A geographical influence model is proposed to filter out locations that are not of interest to the user. By integrating temporal influence and geographical influence a location recommendation algorithm called sPCLR is proposed to recommend locations to the users at a given time of the day. Experimental results show that the sPCLR algorithm performs better than three existing location recommendation algorithms.en_US
dc.language.isoeng
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.classificationCheck-inen_US
dc.subject.classificationLocation Categoryen_US
dc.titleLocation Recommendation on Location Based Social Networks Utilizing Check-in Data and Location Category
dc.typemaster thesis
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26780
thesis.degree.nameMaster of Science
thesis.degree.nameMS
thesis.degree.nameMSc
thesis.degree.disciplineGeomatics Engineering
thesis.degree.grantorUniversity of Calgary
atmire.migration.oldid2560
dc.publisher.placeCalgaryen
ucalgary.item.requestcopytrue


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University 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.