Web Mining
From news websites to tweets and reviews, the multitude of rich information sources available on the Web today provides wonderful opportunities and challenges to Web mining for a diverse range of applications: constructing large knowledge bases, predicting the future, estimating the spread of a disease in a population, evaluating and summarizing opinions and reviews, as well as filtering and cleaning Web content to improve the experience of users consuming it. We welcome submissions of original high-quality research papers related to all aspects of Web Mining, including, but not limited to, the topics below.
Topics
- Association analysis
- Clustering and classification of text, image, videos, Web pages and metadata
- Change detection and monitoring methods
- Query log, click trail, and traffic data mining
- Mining tweets, tags, and multimodal data
- Mining opinions and related data
- Detecting Web spam, opinion spam and fake reviews
- Recommendation systems
- Data integration and data cleaning
- Predicting the future using social media and other Web data
- Efficient algorithms for big data analysis
- Machine learning and data mining techniques for data analysis
- Online advertising
- Distributed and Parallel algorithms using MapReduce
- Other novel Web mining applications and algorithms
Track chairs
- Tanya Berger-Wolf (UIC)
- Bing Liu (UIC)
- Kyuseok Shim (SNU)
TPC members
- Alexandros Ntoulas (Zynga)
- Amr Ahmed (Yahoo! Research)
- Aristides Gionis (Yahoo! Research Barcelona)
- Arjun Mukherjee (University of Illinois, Chicago)
- Bamshad Mobasher (DePaul University)
- C. Lee Giles (Pennsylvania State University)
- Charu Aggarwal (IBM)
- Chin-Wan Chung (KAIST)
- Christos Faloutsos (Carnegie Mellon University)
- David Crandall (Indiana University)
- Dimitrios Gunopulos (UoA)
- Dongwon Lee (Penn State University)
- Dou Shen (Microsoft Adcenter Labs)
- Dunja Mladenic (J.Stefan Institute)
- Eduard
- Gao Cong (Nanyang Technological University)
- Georges Dupret (yahoo! Labs)
- Hady Lauw (Singapore Management University)
- Hang Li (Huawei Noah’s Arc Lab)
- Huan Liu (Arizona State University)
- Hui Xiong (Rutgers University)
- Hwanjo Yu (POSTECH)
- Irwin King (The Chinese University of Hong Kong)
- Jaewoo Kang (Korea University)
- Jaideep Srivastava (University of Minnesota)
- Jeffery Xu Yu (Department of Systems Engineering & Engineering Management, Chinese University of Hong Kong)
- Jian Pei (Simon Fraser University)
- Jianyong Wang (Tsinghua University)
- Jiawei Han (University of Illinois at Urbana-Champaign)
- Ke Wang (Simon Fraser University)
- Lei Zhang (University of Ilinois at Chicago)
- Lei Tang (@WalmartLabs)
- Mark Sandler (Google)
- Martin Ester (Department of Computer Science, Simon Fraser University, Canada)
- Masashi Toyoda (University of Tokyo)
- Mayank Lahiri (University of Illinois at Chicago)
- Mayur Datar (Google Inc)
- Michael Lyu ()
- Michael Wurst (TU Dortmund)
- Michael Gamon (Microsoft Research)
- Michalis Vazirgiannis (Department of Informatics, AUEB)
- Myra Spiliopoulou (U. Magdeburg)
- Natalie Glance (Google)
- Nitin Jindal (Google)
- Olfa Nasraoui (Univ of Louisville)
- Philip Yu (UIC)
- Roberto Bayardo (Google)
- Ronen Feldman (Hebrew University)
- Sangkeun Lee (Department of Computer Sciene and Engineering, Korea University)
- Sang-Wook Kim (Hanyang University)
- Shawndra Hill (University of Pennsylvania)
- Smriti Bhagat (Technicolor)
- Srinivasan Parthasarathy (Department of Computer Science and Engineering, The Ohio State University)
- Tie-Yan Liu (Microsoft Research Asia)
- Tina Eliassi-Rad (Rutgers University)
- U Kang (Carnegie Mellon University)
- Wai Lam (The Chinese University of Hong Kong)
- Wook-Shin Han (Kyungpook National University)
- Xiaoli Li (Institute for Infocomm Research)
- Yue Lu (University of Illinois at Urbana-Champaign)
- Yun Chi (NEC Laboratories America)
- Zaiqing Nie (Microsoft Research Asia)
