SWIRL (http://www.cs.rmit.edu.au/swirl12/index.php) is a workshop series that aims to explore the long-range issues in information retrieval. The 2012 meeting will take place next month in Lorne, Victoria, Australia.
Participants were asked to nominate three papers that “represent important new directions, research areas, or results in the IR field.” Since highlighting the bleeding (and soon-to-be-bleeding) edge is a goal of NR, I decided to mirror the list of nominated papers below.
(Original list is at http://www.cs.rmit.edu.au/swirl12/proceedings.php)
What do you think of this list? Are there other papers you feel should be on this list? (Undoubtedly there are, as this is the expressly limited input of a few participants!) What do you think is the research that has the strongest implications for IR research for the next few years?
- Stuff I’ve seen: a system for personal information retrieval and re-use.
Susan Dumais, Edward Cutrell, JJ Cadiz, Gavin Jancke, Raman Sarin, and Daniel C. Robbins
In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’03). ACM, New York, NY, USA, 72-79, 2003.
- A unified and discriminative model for query refinement
J. Guo, G. Xu, H. Li, and X. Cheng
In SIGIR08, pages 379–386, Singapore, 2008.
- Worker types and personality traits in crowdsourcing relevance labels.
Gabriella Kazai, Jaap Kamps and Natasa Milic-Frayling
In Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM 2011), 4 pages, 2011.
- The Context-Aware Browser.
Paolo Coppola, Vincenzo Della Mea, Luca Di Gaspero, Davide Menegon, Danny Mischis, Stefano Mizzaro, Ivan Scagnetto and Luca Vassena.
IEEE Intelligent Systems 25(1): 38-47 (2010).
- Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets During Mass Emergency.
Sudha Verma, Sarah Vieweg, William Corvey, Leysia Palen, James H. Martin, Martha Palmer, Aaron Schram and Kenneth M. Anderson.
In Fifth International AAAI Conference on Weblogs and Social Media, 2011.
- IR System Evaluation using Nugget-based Test Collections
Virgil Pavlu, Shahzad Rajput, Peter B. Golbus and Javed A. Aslam
WSDM 2012, to appear
- Evaluating Search Systems Using Result Page Context
Peter Bailey, Nick Craswell, Ryen W. White, Liwei Chen, Ashwin Satyanarayana, and S. M. M. Tahaghoghi
IIiX 2010 pp.105-114
- Good Abandonment in Mobile and PC Internet Search
Jane Li, Scott B. Huffman, and Akihito Tokuda
SIGIR 2009 pp.43-50
- The Web changes everything: Understanding the dynamics of Web content.
E. Adar, J. Teevan, S. Dumais and J. Elsas (2009).
In Proceedings of WSDM 2009.
- Time Challenges – Challenging Times for Future Information Search.
Thomas Mestl, Olga Cerrato, Jon Ølnes, Per Myrseth, Inger-Mette Gustavsen.
D-Lib Magazine, May/June 2009, Vol. 15, No. 5/6.
- How to build a WebFountain: An architecture for very large-scale text analytics.
D. Gruhl, L. Chavet, D. Gibson, J. Meyer, P. Pattanayak, A. Tomkins, and J. Zien. 2004.
IBM Syst. J. 43, 1 (January 2004), 64-77. DOI=10.1147/sj.431.0064
Accepted SIGIR 2011 authors have started posting their papers online, so I have decided to collect them here. Please comment on the ones you think are the most interesting and important. As I get more papers, I’ll edit this post to add them.
http://bit.ly/iYr7HB Nima Asadi, Don Metzler, Tamer Elsayed, and Jimmy Lin, “Pseudo Test Collections for Learning Web Search Ranking Functions”
http://bit.ly/l4dBYO Evangelos Kanoulas, Ben Carterette, Paul D. Clough, and Mark Sanderson, “Evaluating Multi-Query Sessions”
http://bit.ly/kj8ZbD Michael Bendersky, Don Metzler, and Bruce Croft, “Parameterized Concept Weighting in Verbose Queries”
http://bit.ly/iAel3e Rodrygo L. T. Santos, Craig Macdonald, and Iadh Ounis, “Intent-Aware Search Result Diversification”
http://bit.ly/iTiNhJ Elad Yom-Tov, Fernando Diaz, “Out of sight, not out of mind: On the effect of social and physical detachment on information need”
http://bit.ly/jxV5BV Tetsuya Sakai and Ruihua Song, “Evaluating Diversified Search Results Using Per-intent Graded Relevance”
http://bit.ly/jTrdQD Ferhan Ture, Tamer Elsayed, and Jimmy Lin. “No Free Lunch: Brute Force vs. Locality-Sensitive Hashing for Cross-lingual Pairwise Similarity”
http://bit.ly/loeoAh Ben Carterette, “System Effectiveness, User Models, and User Utility: A Conceptual Framework for Investigation”
http://bit.ly/jH2vNX Manos Tsagkias, Maarten de Rijke, and Wouter Weerkamp, “Hypergeometric Language Models for Republished Article Finding”
http://bit.ly/kVfJpQ Wouter Weerkamp, Bogomil Kovachev, Richard Berendsen, Edgar Meij, Krisztian Balog, and Maarten de Rijke, “People Searching for People: Analysis of a People Search Engine Log”
http://bit.ly/iX0ZZj Daveid Elsweiler, Morgan Harvey, Martin Hacker, “Understanding Re-finding behavior in Naturalistic Email Interaction Logs”
http://bit.ly/loqQ57 David Elsweiler, David E. Losada, José Carlos Toucedo, Ronald T. Fernández, “Seeding Simulated Queries with User-study Data for Personal Search Evaluation”
http://tinyurl.com/3bsxql5 Aleksander Stupar and Sebastian Michel, “PICASSO – To Sing you must Close Your Eyes and Draw”
http://bit.ly/kYjT39 Avishek Anand, Srikanta Bedathur, Klaus Berberich, Ralf Schenkel, “Temporal Index Sharding for Space-Time Efficiency in Archive Search”
Gordon V. Cormack, Mark D. Smucker, and Charles L. A. Clarke
University of Waterloo
The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general Web in early 2009. This dataset contains 1 billion web pages, a substantial fraction of which are spam — pages designed to deceive search engines so as to deliver an unwanted payload. We examine the effect of spam on the results of the TREC 2009 web ad hoc and relevance feedback tasks, which used the ClueWeb09 dataset.
We show that a simple content-based classifier with minimal training is efficient enough to rank the “spamminess” of every page in the dataset using a standard personal computer in 48 hours, and effective enough to yield significant and substantive improvements in the fixed-cutoff precision (estP10) as well as rank measures (estR Precision, StatMAP, MAP) of nearly all submitted runs. Moreover, using a set of “honeypot” queries the labeling of training data may be reduced to an entirely automatic process. The results of classical information retrieval methods are particularly enhanced by filtering — from among the worst to among the best.