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, finding trends and epidemic in a population, marketing and recommendation, as well as filtering and cleaning Web content to improve the experience of users consuming it. This track covers data analysis for a wide variety of Web data including tweets, tags, links, logs, images, videos, and other multimodal data. 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, classification and other mining and analysis of Web data
- Big data analysis integrated with Web data
- Change detection and monitoring methods
- Data integration and data cleaning
- Detecting Web spam, opinion spam and fake reviews
- Distributed and parallel algorithms for large scale data mining
- Machine learning and data mining techniques for data analysis
- Predicting the future using Web data
- Recommendation systems
- Query log, click trail, and traffic data mining
- Structured data extraction from Web data
- Other novel Web mining applications and algorithms such as deep learning
Area Chairs
- Rajeev Rastogi (Amazon)
- Yutaka Matsuo (University of Tokyo)
TPC Members
- Azin Ashkan (University of Waterloo)
- Smriti Bhagat (Technicolor)
- Mikhail Bilenko (Microsoft Research)
- Michael Cafarella (University of Michigan)
- Rui Cai (Microsoft Research Asia)
- Vineet Chaoji (Amazon)
- Sanjay Chawla (University of Sydney)
- Yun Chi (NEC Labs)
- Bee-Chung Chen (LinkedIn)
- Brian Davison (Lehigh University)
- Eduard Dragut (Temple University)
- Minos Garofalakis (Technical University of Crete, Chania)
- Natalie Glance (Google)
- Qi He (LinkedIn)
- Ralf Herbrich (Amazon)
- Daxin Jiang (Microsoft Research Asia)
- Jaewoo Kang (Korea University)
- Anitha Kannan (Microsoft Research)
- Evangelos Kanoulas (Google)
- Sang-Wook Kim (Hanyang University)
- Yehuda Koren (Google)
- Christian Arnd Konig (Microsoft Research)
- Nick Koudas (University of Toronto)
- Mayank Lahiri (University of Illinois, Chicago)
- Wai Lam (Chinese University of Hong Kong)
- Hady Lauw (Singapore Management University)
- Chengkai Li (University of Texas, Arlington)
- Xiaoli Li (Institute for Infocomm Research)
- Ee-Peng Lim (Singapore Management University)
- Huan Liu (Arizona State University)
- Yue Lu (University of Illinois, Urbana Champaign)
- Michael Lyu (Chinese University of Hong Kong)
- Hao Ma (Microsoft Research)
- Srujana Merugu (Amazon)
- Vahab Mirrokni (Google)
- Bamshad Mobasher (DePaul University)
- Arjun Mukherjee (University of Illinois, Chicago)
- Zaiqing Nie (Microsoft Research Asia)
- Alexandros Ntoulas (Microsoft Research)
- Umut Ozertem (Microsoft Research)
- Rina Panigrahy (Microsoft Research)
- Nish Parikh (Ebay Research Labs)
- Srinivasan Parthasarthy (Ohio State University)
- Jian Pei (Simon Fraser University)
- Simone Ponzetto (Universitat Mannheim)
- Kira Radinsky (Technion)
- Naren Ramakrishnan (Virginia Tech)
- B. Ravindran (IIT Madras)
- Mark Sandler (Google)
- Sunita Sarawagi (IIT Bombay)
- Venu Satuluri (Twitter)
- Dou Shen (Microsoft Adcenter Labs)
- Myra Spiliopoulou (U. Magdeburg)
- Jaideep Srivastava (University of Minnesota)
- Neel Sundaresan (Ebay Research Labs)
- Lei Tang (Walmart Labs)
- Masashi Toyoda (University of Tokyo)
- Michalis Vazirgiannis (AUEB)
- S. V. N. Vishwanathan (Purdue University)
- Jianyong Wang (Tsinghua University)
- Ke Wang (Simon Fraser University)
- Michael Wurst (TU Dortmund)
- Hui Xiong (Rutgers University)
- Shuang-Hong Yang (Twitter)
- Emine Yilmaz (Microsoft Researchv)
- Jeffery Xu Yu (Chinese University of Hong Kong)
- Philip Yu (University of Illinois, Chicago)
- Mohammed Zaki (RPI)
- Lei Zhang (University of Illinois, Chicago)
- Zhe Zhao (University of Michigan)
- Feida Zhu (Singapore Management University)
