Andrew Trotman (University of Otago, New Zealand), firstname.lastname@example.org
Shlomo Geva (Queensland University of Technology, Australia), email@example.com
Jaap Kamps (University of Amsterdam, The Netherlands), firstname.lastname@example.org
Standard document retrieval finds atomic documents, and leaves it to the end-user to
locate the relevant information inside the document. Focused retrieval removes this
latter task from the end user by providing more direct access to relevant information.
That is, focused retrieval addresses information retrieval and not simply document
retrieval. Focused retrieval has already been used to extract relevant sections from
academic documents, and the application to text book search is obvious.
The purpose of this workshop is to raise issues and promote discussion on focused retrieval - that is, Question Answering, Passage Retrieval, Element Retrieval, and other models. Unexpected synergies have already come to light and we expect the workshop papers and discussions to be lively and highly productive.
Hang Li (Microsoft Research Asia), email@example.com
Tie-Yan Liu (Microsoft Research Asia), Tie-Yan.Liu@microsoft.com
ChengXiang Zhai (University of Illinois at Urbana-Champaign), firstname.lastname@example.org
The task of "learning to rank" has emerged as an active and growing area of research both in information retrieval and machine learning. The goal is to design and apply methods to automatically learn a function from training data, such that the function can sort objects (e.g., documents) according to their degrees of relevance, preference, or importance as defined in a specific application.
The relevance of this task for IR is without question, because many IR problems are by nature ranking problems. Improved algorithms for learning ranking functions promise improved retrieval quality and less of a need for manual parameter adaptation. In this way, many IR technologies can be potentially enhanced by using learning to rank techniques.
A workshop entitled Learning to Rank for Information Retrieval will be organized at SIGIR 2008, following a very successful workshop on the same topic at SIGIR 2007 (website: http://research.microsoft.com/users/LR4IR-2007/ report: http://delivery.acm.org/10.1145/1330000/1328974/
p58-joachims.pdf?key1=1328974&key2=3383457021&coll=GUIDE&dl=&CFID=62683274&CFTOKEN=33141712 ). The main purpose of this workshop is to bring together IR researchers and ML researchers working on or interested in the technologies, and let them to share their latest research results, to express their opinions on the related issues, and to discuss future directions.
Joachim Kohler (Fraunhofer IAIS, Germany), email@example.com
Martha Larson (University of Amsterdam, The Netherlands), firstname.lastname@example.org
Franciska de Jong (University of Twente, The Netherlands), email@example.com
Roeland Ordelman (University of Twente, The Netherlands), firstname.lastname@example.org
Wessel Kraaij (TNO ICT, The Netherlands), email@example.com
SSCS 2008 brings together speech and IR researchers. Topics include optimizing search for speech, exploiting evidence beyond the word, access to multilingual/multimedia collections, speech mining, visualization, system design, development and evaluation. Domains of interest encompass conversational broadcast, podcasts, meetings, lectures, discussions, debates, interviews and cultural heritage archives
Daniel Lopresti (Lehigh University), firstname.lastname@example.org
Shourya Roy (IBM India Research Lab), email@example.com
Klaus Schulz (University of Munich), firstname.lastname@example.org
L. Venkata Subramaniam (IBM India Research Lab), email@example.com
By its very nature, noisy text warrants moving beyond traditional text analytics techniques. Noise introduces challenges that need special handling, either through new methods or improved versions of existing ones. We welcome original research papers that identify key problems related to noisy text analytics and offer solutions.
Krisztian Balog (University of Amsterdam, The Netherlands), firstname.lastname@example.org
Yong Yu (Apex Knowledge & Data Management Lab, China), email@example.com
At the TREC Enterprise Track in 2005 the need to study and understand expertise retrieval was recognized through the introduction of an Expert Finding task. The task has generated a lot of interest in the IR community, and rapid progress has been made in terms of modeling, algorithms, and evaluation over the past 3 years. In fact, expertise retrieval has reached the point where it is appropriate to assess progress, bring people from different research communities together, and define a research agenda for the next years.
Mounia Lalmas (Queen Mary, University of London), firstname.lastname@example.org
Vanessa Murdock (Yahoo! Research Barcelona), email@example.com
Aggregated search assembles information from a variety of sources, placing it in a single interface. The workshop seeks an understanding of the current state-of-the-art in aggregated search. We will discuss issues such as ranking and aggregating results from multiple sources, generating interfaces, and evaluating such systems.
Paul N. Bennett (Microsoft Research), Paul.N.Bennett@microsoft.com
Ben Carterette (University of Massachusetts Amherst), firstname.lastname@example.org
Olivier Chapelle (Yahoo! Research), email@example.com
Thorsten Joachims (Cornell University), firstname.lastname@example.org
New response types, like preference judgments or usage data, require learning methods, evaluation measures, and collection procedures designed for them. This workshop will address research challenges at the intersection of novel measures of relevance, novel learning methods, and core evaluation issues. We encourage the submission of new research that extends traditional relevance assessment in evaluation, machine learning, collaborative filtering, and user modeling.
As of 20 May 2008, this workshop has been cancelled. All prior registrants who have selected this workshop will be notified by the SIGIR secretariat and an alternative workshop or refund will be issued.
Kap Luk Chan (Nanyang Technological University, Singapore), EKLCHAN@ntu.edu.sg
Xing Xie (Microsoft Research Asia, China), email@example.com
Wang-Chien Lee (Penn State University, USA), firstname.lastname@example.org
Minoru Etoh (NTT DoCoMo, Japan), email@example.com
Flora S. Tsai (Nanyang Technological University, Singapore), firstname.lastname@example.org
Mobile Information Retrieval (MobIR’08) is a timely workshop concerned with the indexing and retrieval of textual, audio and visual information such as text, graphics, animation, sound, speech, image, video and their various possible combinations for use in mobile devices with wireless network connectivity. The objective of this workshop is to provide a single forum for researchers and technologists to discuss the state-of-the-art, present their contributions, and set future directions in emerging innovative research for mobile information retrieval.
Ewa Dominowska (Microsoft), email@example.com
Eugene Agichtein (Emory University), firstname.lastname@example.org
James Shanahan (Independent Consultant), email@example.com
Evgeniy Gabrilovich (Yahoo! Research), firstname.lastname@example.org
Advertising is a multi-billion dollar industry that has become a significant component of the Web browsing experience. Online advertising systems incorporate many information retrieval techniques by combining content analysis, user interaction models, and commercial constraints. Advances in online advertising have come from integrating several core research areas: information retrieval, data mining, machine learning, and user modeling.
The workshop will serve as an open forum for discussion of new ideas and current research related to information retrieval topics relevant to online advertising. The outcome will be a set of full and short papers covering a variety of topics. The short paper format will allow researchers new to the area to actively participate and explore novel themes. It will also enable researchers without access to extensive empirical data to propose ideas and experiments. We also expect the workshop to help develop a community of researchers interested in this area, and yield future collaboration and exchanges.
Despite its commercial significance, advertising is a rather young field of research. This workshop will help the emerging research community better organize and develop a common perspective. The workshop will serve as a forum for researchers and industry participants to exchange latest ideas and best practices while encouraging future breakthroughs. It will also aid in fostering collaboration between industry and academia.