As an NLP researcher, I see lots of NLP papers on top of AAAI. I think that the high citation count is due to<p>(i) people in NLP actually bothering to tell others why their approach is interesting<p>(ii) other people being interested in the same / a similar kind of thing [avoiding the discipline-of-one problem that niche AI applications would have] and<p>(iii) NLP having a reasonably developed "canon" about what counts as must-cite papers. This canon is heavily biased towards US work, and towards people who write decent explanations of what they do, but at least it makes sure that people know about the big problems and failed (or not-quite-failed-as-badly-as-the-others) solutions.<p>What you see in other conferences is that the "Best paper" awards get to (i) more theoretical papers which still have issues to solve before people can use the approach (nothing wrong with those!), in (ii) subfields that are currently "hot". Whereas the most-cited papers are (i) more obviously about things that a dedicated person could apply in practice, and (ii) in a subfield that is obscure at the time but will become more popular in the following years.
Reviewing SIGMOD, it appears that a lot of the citations earned are less about innovative research, and more about the everyone using the software tools they published.<p>And a survey paper in the field of big data analysis (survey papers are citation bate, but won't be pulling in many grants or awards).
Is it possible that papers that get the awards help give the scientists new ways of looking at problems, while the papers that are frequently cited are more likely to follow established viewpoints and back it with hard data I can use to justify later experiments?<p>What I mean is, if a paper makes me think "wow, I've never though of this that way before, I wonder if I could try something like that with this...." I probably wouldn't cite it, right? Its not directly related. But I would probably give it an award for best paper because it helped me come up with a new approach to my own problem.<p>disclaimer: I am not a scientist.
Nitpicking, but why are they claiming to provide MAP (mean average precision) scores when their description and equation indicates that they are computing average precision, not MAP. According to the definition of MAP [1] that they link to, MAP is computed across multiple queries while average precision is computed for one [2]. Furthermore, they truncate their calculation to only consider the top 3 cited papers (i.e., they don't go all the way to 100% recall), so it's not even really the average precision.<p>[1] <a href="http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision" rel="nofollow">http://en.wikipedia.org/wiki/Information_retrieval#Mean_aver...</a><p>[2] <a href="http://en.wikipedia.org/wiki/Information_retrieval#Average_precision" rel="nofollow">http://en.wikipedia.org/wiki/Information_retrieval#Average_p...</a>
Conference organizers are well aware that best paper awards are not perfect predictors of importance or popularity. Many top conferences specifically introduced separate awards ("most influential", "test of time", etc.) granted e.g. 10 years after publication.
In 2009, Bartneck et al. did a scientometric analysis of papers presented at CHI - the most prestigious academic HCI conference [1]:<p><i>"The papers acknowledged by the best paper award committee were not cited more often than a random sample of papers from the same years."</i><p>[1] <a href="http://www.bartneck.de/publications/2009/scientometricAnalysisOfTheCHI/" rel="nofollow">http://www.bartneck.de/publications/2009/scientometricAnalys...</a>
I think it's far more interesting to just see what the top cited papers are every year (after the fact) than to compare with the best paper. Best paper awards are given for a lot of reasons that aren't consistent across conferences or even across years of the same conference.
A cursory browse shows an interesting pattern in the names of the researchers.<p>edit_ Perhaps I should clarify: It's entirely possible that exposure in the West has a large part to do with the media, who often don't wade too deeply into scientific matters.