This year's SIGIR best paper award was presented to Mikhail Ageev (Moscow State University), and Qi Guo, Dmitry Lagun, and Eugene Agichtein (Emory University) for their paper Find It If You Can: A Game for Modeling Different Types of Web Search Success Using Interaction Data in which they propose a principled formalization of different types of success for informational search, and a scalable game-like infrastructure for crowdsourcing search behavior studies.
The best student paper award was awarded to Shuang-Hong Yang (Georgia Institute of Technology), Bo Long and Alexander J. Smola (Yahoo! Labs), Hongyuan Zha (Georgia Institute of Technology), and Zhaohui Zheng (Yahoo! Labs Beijing) for their paper Collaborative Competitive Filtering: Learning Recommender using Context of User Choice. The paper proposes Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems.
There were honorable mentions for the papers: Parameterized Concept Weighting in Verbose Queries, Understanding Re-finding Behaviour in Naturalistic Email Interaction Log, Out of sight, not out of mind: On the effect of social and physical detachment on information need, Enhanced Results for Web Search, and Recommending Ephemeral Items at Web Scale.
FACT, Folktales As Classifiable Texts, is a project funded by the NWO Catch program. In the FACT project, the HMI group and DB group of the University of Twente will cooperate with the Meertens Institute to study new possibilities for researchers from humanities disciplines (folktale and narratology researchers, documentalists, etc.) to explore folktales based on annotations and links generated by data-driven methods. To this end, FACT will develop software enabling the computer to automatically enrich a corpus of Dutch folktales with metadata such as names, genre, type, and a summary. In addition, FACT represents the first effort to systematically apply and evaluate various clustering techniques on a very large (40.000+) and diverse collection of folktales. The algorithms developed in the project will be integrated in a user-friendly platform that supports annotation as well as exploratory research into variability in oral and written transmission, using XML database technology to model all folktale data (both annotations and the text of the tale itself) in one unifying framework. A large part of the scientific research in FACT will deal with the pros and cons of human classification and computerized clustering to investigate variation in (oral) transmission. By using document clustering, we hope to discover relationships between documents that cannot be readily identified by human annotators. The main challenge will be to make the computer decide which texts are related and which are not. This is not a black-or-white issue: folktales may be related to each other on different dimensions and to varying degrees. Will the computer be able to recognize the cultural DNA of tales, and make a distinction between different types (no kinship) and versions of the same type (kinship)? 
