Meiqun Hu

Ph.D Candidate

School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902

Fax :	+65 6828 0468
Email :

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Meiqun Hu is a Ph.D Candidate in School of Information Systems, Singapore Management University. Her research focuses on mining Web data in general. Her Ph.D dissertation is titled Predictive Modeling for Nagivating Social Media. She is advised by Professor Ee-Peng Lim and Assistant Professor Jing Jiang.

Meiqun received her Bachelor's degree in Computer Engineering from Nanyang Technological University, Singapore in June 2006.

Meiqun is a student member of ACM and ACL.

About | Publications | CV | Miscellaneous

Research

Research Interest

Meiqun's primary research interest is in modeling the semantic, social and temporal structures in social media. Taking the collaborative social bookmarking system as an example, she studies topic modeling for social tag prediction and trend discovery, as well as user profiling for personalized tag recommendation.

Previously, Meiqun has also worked on modeling the interaction dynamics in Web communities. She studied information quality assessment for collaborative user-created content on Wikipedia, as well as the use of quality assessment in improving content search and recommendation.

An overview of her research and professional interests is given in this set of industry job talk slides.^new

Education

Professional Activities

Teaching Experience

About | Research | CV | Miscellaneous

Publications

Refereed Conferences

A Probabilistic Approach to Personalized Tag Recommendation
Meiqun Hu, Ee-Peng Lim and Jing Jiang
IEEE Second International Conference on Social Computing (SocialCom '10).
[ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 13% ]

In this work, we study the task of personalized tag recommendation in social tagging systems. To reach out to tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for incorporating translations by like-minded users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such similarity measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with personomy translation methods based on the query user solely and collaborative filtering. Our experimental results show that our neighbor based translation methods outperform these baseline methods significantly. Moreover, we show that the translations borrowed from neighbors indeed help ranking relevant tags higher than that based solely on the query user.

@inproceedings{socialcom10hu,
author = {Hu, Meiqun and Lim, Ee-Peng and Jing, Jiang},
title = {A Probabilistic Framework for Personalized Tag Recommendation},
booktitle = {SocialCom '10: Proceedings of the 2010 IEEE Second International Conference on Social Computing},
year = {2010},
location = {Minneapolis, Minnesota, USA},
pages = {33--40},
publisher = {IEEE Computer Society},
}

Predicting Outcome for Collaborative Featured Article Nomination in Wikipedia
Meiqun Hu, Ee-Peng Lim and Ramayya Krishnan
Third International AAAI Conference on Weblogs and Social Media (ICWSM '09).
[ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 15% ]

In Wikipedia, good articles are wanted. While Wikipedia relies on collaborative effort from online volunteers for quality checking, the process of selecting top quality articles is time consuming. At present, the duty of decision making is shouldered by only a couple of administrators. Aiming to assist in the quality checking cycles so as to cope with the exponential growth of online contributions to Wikipedia, this work studies the task of predicting the outcome of featured article (FA) nominations. We analyze FA candidate (FAC) sessions collected over a period of 3.5 years, and examine the extent to which consensus has been practised in this process. We explore the use of interaction features between FAC reviewers to learn SVM classifiers to predict the nomination outcome. We find that, calibrating the individual user's polarity of opinions as features improves the prediction accuracy significantly.

@inproceedings{icwsm09hu,
author = {Hu, Meiqun and Lim, Ee-Peng and Krishnan, Rammaya},
title = {Predicting outcome of collaborative featured article nominations on Wikipedia},
booktitle = {ICWSM '09: Proceedings of the 3rd international AAAI conference on Weblogs and social media},
year = {2009},
location = {San Jose, California, USA},
publisher = {AAAI Press},
}

Measuring Article Quality in Wikipedia: Models and Evaluation
Meiqun Hu, Ee-Peng Lim, Aixin Sun, Hady W. Lauw and Ba-Quy Vuong
Sixteenth ACM Conference on Information and Knowledge Management (CIKM '07).
[ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 17% ]

Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our Basic model is designed based on the mutual dependency between article quality and their author authority. The PeerReview model introduces the review behavior into measuring article quality. Finally, our ProbReview models extend PeerReview with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.

@inproceedings{cikm07hu,
author = {Hu, Meiqun and Lim, Ee-Peng and Sun, Aixin and Lauw, Hady Wirawan and Vuong, Ba-Quy},
title = {Measuring article quality in wikipedia: models and evaluation},
booktitle = {CIKM '07: Proceedings of the sixteenth ACM conference on Information and knowledge management},
year = {2007},
location = {Lisbon, Portugal},
pages = {243--252},
publisher = {ACM},
}

Refereed Workshops

Using Social Annotations for Trend Discovery in Scientific Publications
Meiqun Hu, Ee-Peng Lim and Jing Jiang
Fifth Workshop on Human-Computer Interaction and Information Retrieval (HCIR '11).
[ abstract ] [ paper ] [ poster ] [ bibtex ]

Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform topic-specific trend analysis, we further adopt topic modeling on the annotation content to decapsulate the multitude of impact created by the annotated work.

@inproceedings{socialcom10hu,
author = {Hu, Meiqun and Lim, Ee-Peng and Jing, Jiang},
title = {Using Social Annotations for Trend Discovery in Scientific Publications},
booktitle = {HCIR '11: Proceedings of the Fifth Workshop on Human-Computer Interaction and Information Retrieval},
year = {2011},
location = {Mountain View, California, USA},
howpublished = {Online},
}

On Improving Wikipedia Search using Article Quality
Meiqun Hu, Ee-Peng Lim, Aixin Sun, Hady W. Lauw and Ba-Quy Vuong
Ninth ACM International Workshop on Web Information and Data Management (WIDM '07).
[ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 25% ]

Wikipedia is presently the largest free-and-open online encyclopedia collaboratively edited and maintained by volunteers. While Wikipedia offers full-text search to its users, the accuracy of its relevance-based search can be compromised by poor quality articles edited by non-experts and inexperienced contributors. In this paper, we propose a framework that re-ranks Wikipedia search results considering article quality. We develop two quality measurement models, namely Basic and PeerReview, to derive article quality based on co-authoring data gathered from articles' edit history. Compared with Wikipedia's full-text search engine, Google and Wikiseek, our experimental results showed that (i) quality-only ranking produced by PeerReview gives comparable performance to that ofWikipedia andWikiseek; (ii) PeerReview combined with relevance ranking outperforms Wikipedia's full-text search significantly, delivering search accuracy comparable to Google.

@inproceedings{widm07hu,
author = {Hu, Meiqun and Lim, Ee-Peng and Sun, Aixin and Lauw, Hady Wirawan and Vuong, Ba-Quy},
title = {On improving wikipedia search using article quality},
booktitle = {WIDM '07: Proceedings of the 9th annual ACM international workshop on Web information and data management},
year = {2007},
location = {Lisbon, Portugal},
pages = {145--152},
publisher = {ACM},
}

About | Research | Publications | CV

Miscellaneous

Name Meiqun is her given name. It is pronounced as /meichün/.
Her full name in Chinese is .

Home Town Meiqun was born in the city of Xi'an, Shaanxi, P.R.China. Before coming to Singapore to attend college, she has spent eighteen years in Xi'an, whose ancient civilization has cultivated her in many ways. She loves her home town.

Favorite Links piled higher and deeper

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brilliant industry research

Ph.D Candidate	2008 - 2012 School of Information Systems, Singapore Management University Meiqun defended her Ph.D dissertation on November 30, 2011. Here is her defense talk slides.^new
Ph.D Candidate	2006 - 2008 School of Computer Engineering, Nanyang Technological University Meiqun transferred to Singapore Management University in June 2008.
Bachelor of Engineering	2002 - 2006 School of Computer Engineering, Nanyang Technological University Meiqun graduated with first-class honours in June 2006.

Coordinator	Reading Group Seminar for Social Network Mining Research Fall and Spring, AY 2008 - 2009 School of Information Systems, Singapore Management University
External Reviewer	DASFAA 2010 , JCDL 2010

	IS200 - Software Foundations	Fall, AY 2009 - 2010
	IS424 - Data Mining and Business Analytics	Spring, AY 2008 - 2009

	CPE303 - Database Systems	Fall, AY 2007 - 2008
	CPE105 - Data Structure and Object-Oriented Programming	Spring, AY 2006 - 2007
	CPE303 - Database Systems	Fall, AY 2006 - 2007

		A Probabilistic Approach to Personalized Tag Recommendation Meiqun Hu, Ee-Peng Lim and Jing Jiang IEEE Second International Conference on Social Computing (SocialCom '10). [ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 13% ] In this work, we study the task of personalized tag recommendation in social tagging systems. To reach out to tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for incorporating translations by like-minded users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such similarity measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with personomy translation methods based on the query user solely and collaborative filtering. Our experimental results show that our neighbor based translation methods outperform these baseline methods significantly. Moreover, we show that the translations borrowed from neighbors indeed help ranking relevant tags higher than that based solely on the query user. @inproceedings{socialcom10hu, author = {Hu, Meiqun and Lim, Ee-Peng and Jing, Jiang}, title = {A Probabilistic Framework for Personalized Tag Recommendation}, booktitle = {SocialCom '10: Proceedings of the 2010 IEEE Second International Conference on Social Computing}, year = {2010}, location = {Minneapolis, Minnesota, USA}, pages = {33--40}, publisher = {IEEE Computer Society}, }

		Predicting Outcome for Collaborative Featured Article Nomination in Wikipedia Meiqun Hu, Ee-Peng Lim and Ramayya Krishnan Third International AAAI Conference on Weblogs and Social Media (ICWSM '09). [ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 15% ] In Wikipedia, good articles are wanted. While Wikipedia relies on collaborative effort from online volunteers for quality checking, the process of selecting top quality articles is time consuming. At present, the duty of decision making is shouldered by only a couple of administrators. Aiming to assist in the quality checking cycles so as to cope with the exponential growth of online contributions to Wikipedia, this work studies the task of predicting the outcome of featured article (FA) nominations. We analyze FA candidate (FAC) sessions collected over a period of 3.5 years, and examine the extent to which consensus has been practised in this process. We explore the use of interaction features between FAC reviewers to learn SVM classifiers to predict the nomination outcome. We find that, calibrating the individual user's polarity of opinions as features improves the prediction accuracy significantly. @inproceedings{icwsm09hu, author = {Hu, Meiqun and Lim, Ee-Peng and Krishnan, Rammaya}, title = {Predicting outcome of collaborative featured article nominations on Wikipedia}, booktitle = {ICWSM '09: Proceedings of the 3rd international AAAI conference on Weblogs and social media}, year = {2009}, location = {San Jose, California, USA}, publisher = {AAAI Press}, }

		Measuring Article Quality in Wikipedia: Models and Evaluation Meiqun Hu, Ee-Peng Lim, Aixin Sun, Hady W. Lauw and Ba-Quy Vuong Sixteenth ACM Conference on Information and Knowledge Management (CIKM '07). [ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 17% ] Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our Basic model is designed based on the mutual dependency between article quality and their author authority. The PeerReview model introduces the review behavior into measuring article quality. Finally, our ProbReview models extend PeerReview with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement. @inproceedings{cikm07hu, author = {Hu, Meiqun and Lim, Ee-Peng and Sun, Aixin and Lauw, Hady Wirawan and Vuong, Ba-Quy}, title = {Measuring article quality in wikipedia: models and evaluation}, booktitle = {CIKM '07: Proceedings of the sixteenth ACM conference on Information and knowledge management}, year = {2007}, location = {Lisbon, Portugal}, pages = {243--252}, publisher = {ACM}, }

		Using Social Annotations for Trend Discovery in Scientific Publications Meiqun Hu, Ee-Peng Lim and Jing Jiang Fifth Workshop on Human-Computer Interaction and Information Retrieval (HCIR '11). [ abstract ] [ paper ] [ poster ] [ bibtex ] Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform topic-specific trend analysis, we further adopt topic modeling on the annotation content to decapsulate the multitude of impact created by the annotated work. @inproceedings{socialcom10hu, author = {Hu, Meiqun and Lim, Ee-Peng and Jing, Jiang}, title = {Using Social Annotations for Trend Discovery in Scientific Publications}, booktitle = {HCIR '11: Proceedings of the Fifth Workshop on Human-Computer Interaction and Information Retrieval}, year = {2011}, location = {Mountain View, California, USA}, howpublished = {Online}, }

		On Improving Wikipedia Search using Article Quality Meiqun Hu, Ee-Peng Lim, Aixin Sun, Hady W. Lauw and Ba-Quy Vuong Ninth ACM International Workshop on Web Information and Data Management (WIDM '07). [ abstract ] [ paper ] [ slides ] [ bibtex ] [ acceptance rate : 25% ] Wikipedia is presently the largest free-and-open online encyclopedia collaboratively edited and maintained by volunteers. While Wikipedia offers full-text search to its users, the accuracy of its relevance-based search can be compromised by poor quality articles edited by non-experts and inexperienced contributors. In this paper, we propose a framework that re-ranks Wikipedia search results considering article quality. We develop two quality measurement models, namely Basic and PeerReview, to derive article quality based on co-authoring data gathered from articles' edit history. Compared with Wikipedia's full-text search engine, Google and Wikiseek, our experimental results showed that (i) quality-only ranking produced by PeerReview gives comparable performance to that ofWikipedia andWikiseek; (ii) PeerReview combined with relevance ranking outperforms Wikipedia's full-text search significantly, delivering search accuracy comparable to Google. @inproceedings{widm07hu, author = {Hu, Meiqun and Lim, Ee-Peng and Sun, Aixin and Lauw, Hady Wirawan and Vuong, Ba-Quy}, title = {On improving wikipedia search using article quality}, booktitle = {WIDM '07: Proceedings of the 9th annual ACM international workshop on Web information and data management}, year = {2007}, location = {Lisbon, Portugal}, pages = {145--152}, publisher = {ACM}, }

Name	Meiqun is her given name. It is pronounced as /meichün/. Her full name in Chinese is .
Home Town	Meiqun was born in the city of Xi'an, Shaanxi, P.R.China. Before coming to Singapore to attend college, she has spent eighteen years in Xi'an, whose ancient civilization has cultivated her in many ways. She loves her home town.
Favorite Links	piled higher and deeper exchange ideas \| share knowledge brilliant industry research