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研究生:陳世穎
研究生(外文):Shih-Ying Chen
論文名稱:以排序演算法整合多異質社群網路使用者
論文名稱(外文):Learning to Rank on Anchor Link across Multiple Heterogeneous Social Networks
指導教授:陳建錦陳建錦引用關係
指導教授(外文):Chien-Chin Chen
口試委員:陳孟彰蔡銘峰盧信銘
口試委員(外文):Meng-Chang ChenMing-Feng TsaiHsin-Min Lu
口試日期:2016-07-07
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:39
中文關鍵詞:排序學習法帳號鏈結雙邊匹配社群網路社交關係行為模型
外文關鍵詞:learning to rankanchor linkbipartite matchingsocial networksocial relationshipbehavior modeling
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隨著科技和技術的發展,社群網路越趨成熟及普及,並已和人們的生活密不可分,無論是基本資料、活躍的時段、出現的地點或是與朋友間的互動等資訊,都可以透過社群網路獲得,進而了解一個人。然而,當人們使用社群網路的習慣從單一變成多元,要從單一社群網路全面了解一個人變得更加困難。因此近期,有些研究開始探討,如何將多社群網路上的帳號進行連結,預測這些帳號是否來自真實世界同一個人。透過帳號連結,服務提供者可以完整地了解使用者,提供更準確的服務以及推薦。本研究針對任兩個帳號進行三個面向的特徵選取,包括基本資訊比對、社群關係以及行為一致性,並且透過排序學習法搭配一對一配對的限制,來進行帳號配對的預測。最後以現實世界中的資料,兩個著名的社群網路 (Google+和Twitter) 進行實驗,並且以查準率、查全率、準確率以及F值進行評估,可以發現本方法超越許多現行的方法,最後我們也將分析特徵的影響力,以及效能提升的關鍵。

With the prevalence of mobile communication techniques, people now spend a lot of time diving in online social networks for various purposes. Social networks thus contain abundant information of users, such as active periods, emerging locations, and interactions with friends. However, as the services social networks provided are orthogonal, no single network comprehensively depicts a user. Recently, a number of researches start to discover the alignments between entities from different social networks. The discovered alignments are valuable as they reveal intentions of users from different perspectives and are helpful to service providers to offer customized services. In this paper, we investigate the alignment problem of users between different social networks. Three aspects of features including profile matching, social relationship and behavior consistency, and techniques of learning to rank with mapping constraints are applied. We resolve the class skewness problem which generally exists in social networks due to the lack of sufficient negative links by learning to rank. Extensive experiments based on two popular heterogeneous social networks (Google+ and Twitter) with evaluation metrics, precision, recall, accuracy and f-measure, are applied to illustrate performance of our method. Also analysis on the features and constraint gives practical implications for future research.

CONTENTS

口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
Chapter 1 Introduction 1
Chapter 2 Literature Review 4
2.1 Features for Finding Anchor Links 4
2.2 Methods Employed 6
Chapter 3 Our Proposed Technique 8
3.1 Candidate Generation 10
3.1.1 Graph-based Method (Social propagation): 11
3.1.2 Model-based Method: 12
3.1.3 Method Comparison 12
3.2 Supervised Method – Ranking Model 13
3.2.1 Pairwise Learning – RankBoost 13
3.2.2 Mapping Constraint – One to One Mapping 16
3.3 Feature Extraction 17
3.3.1 Profile Matching 17
3.1.1 Social Relationship 18
3.3.2 Behavior Consistency 21
Chapter 4 Experiments and Analysis 25
4.1 Dataset Description 25
4.2 Experiment Setting 28
4.2.1 Evaluation Metrics 28
4.2.2 Candidate Generation 29
4.3 Feature Extraction 30
4.4 Performance Comparison 31
4.4.1 Comparison Methods 31
4.4.2 Experiment Result 33
Chapter 5 Conclusion and Future Work 36
REFERENCE 38



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