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« Refereed Papers Track »
| Abuse, Security, and Privacy |Behavioral Analysis and Personalization |
| Bridging Structured and Unstructured Data | Content Analysis |
| Relevance and Ranking | Search Systems and Applications |
Semantic Web |Social Systems and Graph Analysis
| User Interaction and Mobility | Monetization |
| Performance, Scalability, and Availability | Software Infrastructure |
| Web For Emerging Regions |

| Developers Track | Panels | Posters | Tutorials | Workshops |
| PhD Symposium |
Demos | W3C |


Ranking plays a critical role in modern search engines. Continuous innovations in ranking technology have yielded considerable improvements in search engine quality in recent years. Despite these advances, there is still fertile ground for improvements in areas related to core algorithmic search, including novel theories, advanced ranking features, and improved models. Rapid improvements in ranking technology are only possible as the result of robust evaluation methodologies for assessing search engine quality. Accurate measurement requires metrics that consider the multi-aspectual nature of relevance, which includes context, diversity, effort, and frustration. This calls for novel evaluation methodologies that accurately model user satisfaction.

The Relevance and Ranking track welcomes original, high-quality submissions related to all aspects of relevance and ranking. The relevant topics include, but are not limited to, the following:


  • Retrieval models, similarity measures, features, and ranking (e.g., ranking theory, probabilistic retrieval models, machine learning approaches)
  • Query processing and understanding (e.g., intent, query enrichment, query rewriting)
  • Evaluation, metrics, and measurements (e.g., user satisfaction, diversity, behavioral measures)
  • Matching and relevance-ranking of advertisements (e.g., sponsored search, content match, display advertising)

Track Chairs

  • Donald Metzler, University of Southern California, USA
  • Luo Si, Purdue, USA

Program Committee

  • Javed Aslam, Northeastern University, USA
  • Jing Bai, Microsoft, USA
  • Michael Bendersky, University of Massachusetts, USA
  • Paul Bennett, Microsoft, USA
  • Stefan Buettcher, Google, USA
  • Chris Burges, Microsoft, USA
  • Soumen Chakrabarti, Indian Institute of Technology, India
  • Yi Chang, Yahoo!, USA
  • Hsin-Hsi Chen, National University of Taiwan, Taiwan
  • Kevyn Collins-Thompson, Microsoft, USA
  • W. Bruce Croft, University of Massachusetts, USA
  • Hang Cui, Google, USA
  • Fernando Diaz, Yahoo!, USA
  • Djoerd Himestra, University of Twente, Netherlands
  • Dustin Hillard, Yahoo!, USA
  • Jing Jiang, Singapore Management University, Singapore
  • Oren Kurland, Technion, Israel
  • Tao Li, Florida International University, USA
  • Hang Li, Microsoft, China
  • Chin-Yew Lin, Microsoft, China
  • Yoelle Maarek, Yahoo!, Israel
  • Ray Mooney, University of Texas, USA
  • Jan Pedersen, Microsoft, USA
  • Fuchun Peng, Microsoft, USA
  • Filip Radlinski, Microsoft, USA
  • Dou Shen, Microsoft, USA
  • Milad Shokouhi, Microsoft, UK
  • Alex Smola, Yahoo!, USA
  • Kai Yu, NEC, USA
  • Hongyuan Zha, Georgia Tech, USA
  • Min Zhang, Tsinghua University, China
  • Zhaohui Zheng, Yahoo!, USA
  • Shenghuo Zhu, NEC, USA



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