Publications
Detailed Information
STA : Sybil Types-aware Robust Recommender System
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김종권 | - |
dc.contributor.author | 노태완 | - |
dc.date.accessioned | 2017-07-14T02:58:31Z | - |
dc.date.available | 2017-07-14T02:58:31Z | - |
dc.date.issued | 2015-02 | - |
dc.identifier.other | 000000024846 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123119 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 김종권. | - |
dc.description.abstract | Recently many users refer to various recommender sites when they buy things, movies, music and etc with a rapid development of internet. But there are malicious users (Sybil) to raise or lower ratings of items intentionally in these recommender sites, finally recommender system can recommend incomplete or inaccurate results to normal users. We suggest a recommender algorithm to separate ratings which users generate into normal ratings and outlier ratings and to minimize effects of malicious users. In addition, it provides stable RS about three kinds of models (Random attack, Average attack and Bandwagon attack) which are making problems in Recommender system now. To prove performances of suggesting method, we conducted performance analysis to collect real data (crawling). As a result of performance analysis, it is proved that a performance of suggesting method is good regardless of Sybil size compared to existing algorithms. | - |
dc.description.tableofcontents | Abstract i
Contents ii List of Figures iii List of Tables iv Chapter 1 Introduction 1 1.1 Background 1 1.2 Goal and Contribution 3 1.3 Thesis Organization 4 Chapter 2 Related Work 5 2.1 Recommender System 5 2.2 Robust Recommender System 7 2.3 Sybil Attack Type 9 Chapter 3 System Model 11 3.1 Overview 11 3.2 Notations 13 3.3 Initialization 15 3.4 Sybil User Probability Algorithm 16 3.5 Remove Sybil User from Rating Matrix 21 3.6 Rating Prediction Phase 22 Chapter 4 Evaluation and Analysis 23 4.1 Datasets 23 4.2 Matrix 24 4.3 Experimental Setup 25 4.4 Experimental Results and Analysis 27 Chapter 5 Conclusion 32 Bibliography 33 Abstract in Korean 36 | - |
dc.format | application/pdf | - |
dc.format.extent | 906778 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Recommender system | - |
dc.subject | Sybil attack | - |
dc.subject | Sybil Attack Models | - |
dc.subject | Robust Recommender System | - |
dc.subject.ddc | 621 | - |
dc.title | STA : Sybil Types-aware Robust Recommender System | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | 37 | - |
dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
dc.date.awarded | 2015-02 | - |
- Appears in Collections:
- Files in This Item:
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.