I am currently a visiting student at Carnegie Mellon University. I am working with Professor William W. Cohen on Information Extraction in Twitter.

I am a PhD student in School of Information Systems, Singapore Management University. I received my Bachelor of Computer Science from School of Computing, National University of Singapore in 2007. I started my PhD programme in 2009 under the supervision of Professor Ee-Peng Lim. My main research interests are in modeling of social networks.

Publications

Conferences

• Freddy Chong Tat Chua, Hady W. Lauw and Ee-Peng Lim. Mining Social Dependencies in Dynamic Interaction Networks. Proceedings of the SIAM International Conference on Data Mining, SDM 2012, April 26 - 28, 2012, Anaheim, California, USA. SIAM 2012
  abstract

  User-to-user interactions have become ubiquitous in Web 2.0. Users exchange emails, post on newsgroups, tag web pages, co-author papers, etc. Through these interactions, users co-produce or co-adopt content items (e.g., words in emails, tags in social bookmarking sites). We model such dynamic interactions as a user interaction network, which relates users, interactions, and content items over time. After some interactions, a user may produce content that is more similar to those produced by other users previously. We term this effect social dependency, and we seek to mine from such networks the degree to which a user may be socially dependent on another user over time. We propose a Decay Topic Model to model the evolution of a user’s preferences for content items at the topic level, as well as a Social Dependency Metric that quantifies the extent of social dependency based on interactions and content changes. Our experiments on two user interaction networks induced from real-life datasets show the effectiveness of our approach.
  
  

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• Freddy Chong Tat Chua, Hady W. Lauw and Ee-Peng Lim. Predicting Item Adoption Using Social Correlation. Proceedings of the SIAM International Conference on Data Mining, SDM 2011, April 28 - 30, 2011, Mesa, Arizona, USA. SIAM 2011
  abstract

  Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to incorporate social correlation in order to improve prediction of unobserved item adoptions. We propose the Social Correlation model based on Latent Dirichlet Allocation (LDA) that decomposes the adoption graph into a set of latent factors reflecting user preferences, and a social correlation matrix reflecting the degree of correlation from one user to another. This matrix is learned (rather than pre-assigned), has probabilistic interpretation, and preserves the underlying social network structure. We further devise a Hybrid model that combines a user’s own latent factors with her friends’ for adoption prediction. Experiments on Epinions and LiveJournal data sets show that our proposed models outperform the approach based on latent factors only (LDA).
  
  

  bibtex

  @inproceedings{freddy-sdm, author = {Chua, Freddy Chong Tat and Lauw, Hady W. and Lim, Ee-Peng}, title = {Predicting Item Adoption Using Social Correlation}, booktitle = {SDM '11: Proceedings of the SIAM International Conference on Data Mining}, year = {2011}, location = {Mesa, Arizona, USA}, publisher = {SIAM}, address = {Philadelphia, PA, USA}, }
  
  
• Freddy Chong Tat Chua and Ee-Peng Lim. Trust network inference for online rating data using generative models. In KDD ’10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2010. ACM.
  abstract

  In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF) Model, a novel probabilistic model that generate ratings based on a number of rater's and contributor's factors. We demonstrate that parameters of the model can be learnt by Collapsed Gibbs Sampling. We then apply the model to predict trust and distrust between raters and review contributors using a real data-set. Our experiments have shown that the proposed model is capable of predicting both trust and distrust in a unified way. The model can also determine user factors which otherwise cannot be observed from the rating and trust data.
  
  

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  @inproceedings{1835917, author = {Chua, Freddy Chong Tat and Lim, Ee-Peng}, title = {Trust network inference for online rating data using generative models}, booktitle = {KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining}, year = {2010}, isbn = {978-1-4503-0055-1}, pages = {889--898}, location = {Washington, DC, USA}, doi = {http://doi.acm.org/10.1145/1835804.1835917}, publisher = {ACM}, address = {New York, NY, USA}, }
  
  
• Freddy Chong Tat Chua and Ee-Peng Lim. Modeling Bipartite Graphs Using Hierarchical Structures, In ASONAM ' 11: International Conference on Advances in Social Networks Analysis and Mining.
  abstract

  Bipartite networks are often used to capture the relationships between different classes of objects. To model the structure of bipartite networks, we propose a new hierarchical model based on a hierarchical random graph model originally designed for one-mode networks. The new model can better preserve the network fidelity as well as the assortative and disassortative structures of bipartite networks. We apply the proposed model on some paper-author networks in DBLP to find their optimal hierarchical structures. Using the optimal bipartite hierarchical structure, we regenerate networks that exhibit the similar network properties and degree distribution as the observed networks.
  
  

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  Coming soon.
• Minh-Duc Luu, Ee-Peng Lim, Tuan-Anh Hoang, Freddy Chong Tat Chua. Modeling Diffusion in Social Networks using Network Properties. International AAAI Conference on Weblogs and Social Media (ICWSM) 2012.
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• Bing Tian Dai, Freddy Chong Tat Chua and Ee-Peng Lim. Structural Analysis in Multi- Relational Social Networks. Proceedings of the SIAM International Conference on Data Mining, SDM 2012, April 26 - 28, 2012, Anaheim, California, USA. SIAM 2012
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• Byung-Won On, Ee-Peng Lim, Jing Jiang, Freddy Chong Tat Chua, Viet-An Nguyen, Loo-Nin Teow, Messaging Behavior Modeling in Mobile Social Networks, International Symposium on Social Intelligence and Networking (SIN), Minneapolis, Minnesota, USA, August 20-22, 2010.
  abstract

  Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message (or link) and responding to an incoming message (or link). We propose various models for the two behaviors also known as activeness and responsiveness in both messaging and friendship networks. Our experiments show that the two behaviors are quite distinct from each other although they may be correlated. We also show that active and responsive users enjoy more friendships. Finally, we show that the active and responsive users participate more in messaging about major topics.
  
  

  bibtex

  @article{10.1109/SocialCom.2010.68, author = {Byung-Won On and Ee-Peng Lim and Jing Jiang and Freddy Chong Tat Chua and Viet-An Nguyen and Loo-Nin Teow}, title = {Messaging Behavior Modeling in Mobile Social Networks}, journal ={Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust, 2010 IEEE International Conference on}, volume = {0}, isbn = {978-0-7695-4211-9}, year = {2010}, pages = {425-430}, doi = {http://doi.ieeecomputersociety.org/10.1109/SocialCom.2010.68}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

Journals

Recommended Books

• Convex Optimization, Stephen Boyd and Lieven Vandenberghe ★
• All of Statistics: A Concise Course in Statistical Inference, Larry Wasserman
• All of Nonparametric Statistics, Larry Wasserman
• Learning Bayesian Networks, Richard Neopolitan
• Probabilistic Graphical Models, Daphne Koller and Nir Friedman
• Pattern Recognition and Machine Learning, Christopher Bishop

Some Textbooks I used for Coursework

• Further Mathematics for Economic Analysis, Knut Sydsaeter, Peter Hammond, Atle Seierstad, Arne Strom
• Real Analysis with Economic Applications, Efe A. Ok

Academic Trips

SDM 2012 in Anaheim, CA, USA

ASONAM 2011 in Kaohsiung, Taiwan

PAKDD 2011 in Shenzhen, China

SDM 2011 in Mesa, Arizona, Hilton Phoenix East Mesa, USA

SIGKDD 2010 in Washington D.C., Hyatt Regency Crystal City, USA

A Tour of Mount Vernon in Washington D.C.

A visit to University of Minnesota

CIKM 2009 in Hong Kong, Asia World Expo

Personal life

Water sports in Indonesia, Bali