Thanks to Jeff Dalton for alerting me to SIGIR 2009 announcing the lists of accepted papers and posters. As Jon Elsas points out, the authorship looks quite different this year than from previous years, with industry showing an especially strong presence:
- 38% of the papers have at least one author from Microsoft (21 papers), Yahoo! (7 papers), or Google (3 papers)
- No papers from current UMass researchers (though a number from alumni, and decent representation in the posters)–and the only CMU papers accepted were based on work done during internships.
I’m not sure how to interpret this sudden change. Tighter university budgets? More openness on the part of industry? Regardless, I am excited about the papers. Here are a few (well, ten) paper titles that caught my eye:
- A Comparison of Query and Term Suggestion Features for Interactive Searching
- A Statistical Comparison of Tag and Query Logs
- Building Enriched Document Representations using Aggregated Anchor Text
- Dynamicity vs. Effectiveness: Studying Online Clustering for Scatter/Gather
- Effective Query Expansion for Federated Search
- Enhancing Cluster Labeling Using Wikipedia
- Formulating Effective Queries: An Empirical Study on Effectiveness and Effort
- Telling Experts from Spammers: Expertise Ranking in Folksonomies
- When More Is Less: The Paradox of Choice in Search Engine Use
- Where to Stop Reading a Ranked List? Threshold Optimization using Truncated Score Distributions
The posters look great too! I’m especially curious about these ten:
- A Case for Improved Evaluation of Query Difficulty Prediction
- A Relevance Model Based Filter for removing Bad Ads
- An Evaluation of Entity and Frequency Based Query Completion Methods
- Analysing query diversity
- Cluster-based query expansion
- Evaluating Web Search Using Task Completion Time
- Has Adhoc Retrieval Improved Since 1994?
- Is This Urgent? Exploring Time-Sensitive Information Needs in Community Question Answering
- Relevance Criteria for E-Commerce: A Crowdsourcing-based Experimental Analysis
- When is Query Performance Prediction Effective?
And, of course, I’m gearing up for the Industry Track. More details will be posted soon–of course, you’ll be the first to know.
The Noisy Channel 
2 replies on “SIGIR ’09 Accepted Papers”
No papers from current UMass researchers (though a number from alumni, and decent representation in the posters)
There was a huge turnover in the UMass program in the last few years, with most of the senior grad students leaving from 2006-2008. In the current batch of students, most if not all are < 2 years into the program. I think that explains why there are no UMass papers, more than any budget cuts. The current crop of students hasn’t been around long enough to really build up their research yet.
When More Is Less: The Paradox of Choice in Search Engine Use
Hey, that does sound interesting. I wonder what tack the authors will take. Usually when I hear the paradox of choice quoted in reference to search engines, it is to make the argument that faceted search, or exploratory search, or any number of helpful assistance gives the users too much choice, and therefore are to be avoided.
My perspective is the opposite. When all the search engine offers you is a small blank box, you basically have trillions of choices at your disposal, trillions of decisions to make: What query are you going to enter? How long is it going to be? What query modifiers will you use? The choices are, IMO, overwhelming. The command line offers way too much choice.
On the other hand, by giving the user 10 or 20 or even 50 facets or query modifier suggestions, etc. you are narrowing those trillions of choices down to 1o or 20 or 50. It should be much, much easier, then for the user to use the search engine.
That’s just my opinion, though. I’m looking forward to what the authors of the paper have to say, and what experiments they’ve done.
BTW, I make a similar argument here: http://irgupf.com/2009/03/04/ranked-lists-and-the-paradox-of-choice/
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A while back I talked with Harr Chen and David Karger about their “Less is More” work and suggested it might be valuable to return a set of results where exactly k of them are relevant, rather than at least k (they propose a “k-call” measure). I argued based on the paradox of choice: users want a small set of plausible options, and might actually want to see a few other options that are clearly less desirable. They were intrigued, but we never followed up on the idea.
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