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dc.contributor.advisorChairperson, Graduate Committee: Qing Yangen
dc.contributor.authorXu, Yien
dc.date.accessioned2018-05-09T19:08:43Z
dc.date.available2018-05-09T19:08:43Z
dc.date.issued2017en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/13724
dc.description.abstractAn individual's location history in the real world implies his or her interests and behaviors. Accordingly, people who share similar location histories are likely to have common interest and behavior. This thesis analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.subject.lcshInternet.en
dc.subject.lcshSocial networks.en
dc.subject.lcshInformation storage and retrieval systems.en
dc.subject.lcshData mining.en
dc.titleExploring timeliness for accurate location recommendation on location-based social networksen
dc.typeThesisen
dc.rights.holderCopyright 2017 by Yi Xuen
thesis.degree.committeemembersMembers, Graduate Committee: Qing Yang (chairperson); Binhai Zhu; Brendan Mumey.en
thesis.degree.departmentGianforte School of Computing.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage78en
mus.data.thumbpage11en


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