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POI Recommender System with Various Effects
다양한 영향을 고려한 장소 추천

DC Field Value Language
dc.contributor.advisor김종권-
dc.contributor.authorDaHyeon Han-
dc.date.accessioned2017-07-14T02:36:49Z-
dc.date.available2017-07-14T02:36:49Z-
dc.date.issued2017-02-
dc.identifier.other000000142084-
dc.identifier.urihttps://hdl.handle.net/10371/122698-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 컴퓨터공학부, 2017. 2. 김종권.-
dc.description.abstractSeoul National University
More and more Location-based Social Networks(LBSNs) becomes significantly popular due to generalization of smartphones and tablets. With the high accessibility for LBSNs dataset, Point-of-Interest (POI) recommendation gets lots of attention not only in academic area but also in industry recently. As normal recommendation (move
and product), it has data sparsity problem. In addition, as opposed to normal recommendation, it has to deal with physical distance. Hence, make the problem harder. To address the aforementioned problems, many researchers try to utilize assorted effects such as users preference, temporal effect, geographical effect and social effect. In this paper, we proposed UFPCF-G which utilize Users preference, different types of Friends preference, POI similarity and Users activity based Collaborative Filtering with Global Influence. We also explore extended meaning of friends. Unlike other research, we do not regard social relationship as the solely friends. In addition, we propose structure based and activity based global influence. We execute extensive experiment on two real LBSNs dataset and our experiment results outperform other baseline methods in terms of precision and recall.
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dc.description.tableofcontentsChapter 1. Introduction 1
Chapter 2. Related Work 5
2.1 Recommendation Technique 5
2.2 Recommendation 7
Chapter 3. Proposed Scheme 9
3.1 User-based CF 9
3.2 Friend-based CF 10
3.3 POI-based CF 16
3.4 Global Influence 17
3.4.1 Definition 17
3.4.2 Usage of Global Influence 20
Chapter 4. Experiment 21
4.1 Data Description 21
4.2 Comparison Method 23
4.3 Evaluation Metric 25
4.4 Experiment Result 26
4.5 Impact of Effect 29
Chapter 5. Conclusion 31
Bibliography 32
Abstract in Korean 35
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dc.formatapplication/pdf-
dc.format.extent676154 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectPOI Recommender System-
dc.subjectCollaborative Filtering-
dc.subjectLocation Based Social Networks. Social Network.-
dc.subject.ddc621-
dc.titlePOI Recommender System with Various Effects-
dc.title.alternative다양한 영향을 고려한 장소 추천-
dc.typeThesis-
dc.contributor.AlternativeAuthor한다현-
dc.description.degreeMaster-
dc.citation.pagesvii,36-
dc.contributor.affiliation공과대학 컴퓨터공학부-
dc.date.awarded2017-02-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Theses (Master's Degree_컴퓨터공학부)
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