Web Mining

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.

  • Large-scale analysis of web data, including text, image, video, and metadata
  • Web measurements, evolution and models
  • Clustering, classification, and summarization of Web data
  • Multimodal Web content mining
  • Entity, event and relationship extraction from Web data
  • Data integration and data cleaning
  • Traffic and log analysis
  • Web mining for prediction and recommendation
  • Algorithms and systems for Web-scale mining
  • Learning representations and features from Web data
  • Novel Web mining applications

For questions related to this call, please email: research-mining@www2015.it

Area Chairs

  • Brian Davison, Lehigh University
  • Qiaozhu Mei, University of Michigan
  • Andrew Tomkins, Google

Program Committee

  • Charu Aggarwal, IBM
  • Azin Ashkan, Technicolor Labs
  • Timothy Baldwin, The University of Melbourne
  • Roberto Bayardo, Google Research
  • Michael Bendersky, Google, Inc.
  • Mikhail Bilenko, Microsoft
  • Rui Cai, Microsoft Research, Asia
  • Vineet Chaoji, Amazon
  • Ovi Dan, Microsoft
  • Dennis Fetterly
  • Qi He, LinkedIn
  • Liangjie Hong, Yahoo! Labs
  • Xia (Ben) Hu, Arizona State University
  • Daxin Jiang, Microsoft Research Asia
  • Yunliang Jiang, Twitter
  • Arnd Christian K├Ânig, Microsoft Research
  • Yehuda Koren, Google
  • Wai Lam, The Chinese University of Hong Kong
  • Hady Lauw, Singapore Management University
  • Chengkai Li, University of Texas at Arlington
  • Huan Liu, Professor, Arizona State University
  • Yue Lu, Twitter Inc.
  • Michael Lyu, the Chinese University of Hong Kong
  • Hao Ma, Microsoft Research
  • Yutaka Matsuo, University of Tokyo
  • Srujana Merugu, Amazon
  • Dunja Mladenic, Jozef Stefan Institute
  • Bamshad Mobasher, DePaul University
  • Arjun Mukherjee, University of Illinois, Chicago
  • Olfa Nasraoui, University of Louisville
  • Alexandros Ntoulas, Zynga
  • Spiros Papadimitriou, Rutgers University
  • Srinivasan Parthasarathy,
  • Jian Pei, Simon Fraser University
  • Simone Paolo Ponzetto, University of Mannheim
  • Naren Ramakrishnan, Virginia Tech
  • Rajeev Rastogi, Yahoo!
  • Tamas Sarlos, Google
  • H. Andrew Schwartz, University of Pennsylvania
  • Myra Spiliopoulou, U. Magdeburg
  • Yizhou Sun, Northeastern University
  • Neel Sundaresan, eBay Research Labs
  • Chenhao Tan, Cornell University
  • Jian Tang, Peking University
  • Cindi Thompson
  • Masashi Toyoda, University of Tokyo
  • Jianyong Wang, Tsinghua University
  • Ke Wang, Simon Fraser University
  • Jian Wang, LinkedIn Corporation
  • Hongning Wang, Department of Computer Science at University of Virginia
  • Shuang-Hong Yang, Twitter Inc.
  • Philip Yu, University of Illinois at Chicago
  • Jeffery Xu Yu, Department of Systems Engineering & Engineering Management, Chinese University of Hong Kong
  • Zhe Zhao, University of Michigan, Ann Arbor
  • Feida Zhu, Singapore Management University