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Workshop on Collaborative Information Retrieval (CIR 2011)
CIKM’2011, Glasgow, UK, October 28th.
http://cir2011.fxpal.com/
Organizers
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- Gene Golovchinsky, FX Palo Alto Laboratory, Inc, USA.
- Jeremy Pickens, Catalyst Repository Systems, USA.
- Meredith Ringel Morris, Microsoft Research, USA.
- Juan M. Fernández-Luna, University of Granada, Spain.
- Juan F. Huete, University of Granada, Spain.
- Julio C. Rodríguez-Cano, University of Informatics Science, Cuba.
Introduction and Goal
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This is the third workshop we are organizing on the topic of collaborative information retrieval. The first workshop, held in conjunction with JCDL 2008, focused on broad topics and sought to establish a vocabulary for discussion about collaborative information seeking, to identify work practices and disciplines that might benefit from collaborative information seeking, and to establish a community of researchers with related interests. The second workshop, held in conjunction with CSCW 2010, built on the previous results, and focused on issues of communication and awareness in support of collaborative information seeking.
Our goal in this third workshop is to focus on algorithmic and other software issues related to information seeking in a collaborative setting. Continue reading…
I would like to quickly follow up on my previous post on explicitly collaborative information seeking. My claim in that post was that, despite the shared terminology, a service like Aardvark (or Twitter) is not truly collaborative.
Let me be clear about Aardvark: What that service does is help you comb through a network [...]
A panel on Social Search is happening at SXSW right now. Reading Danny Sullivan’s liveblogging, I came across the panel’s definition of the three distinct types of social searching. And I think they left one out:
Collective (gathering advice from a crowd) Friend Filtered (using your friends) Collaborative (asking a friend — see also [...]
Greg Linden has an interesting post on Search on a domain like YouTube. I reproduce it here because I would like to elaborate on it:
The article focuses on YouTube’s “plans to rely more heavily on personalization and ties between users to refine recommendations” and “suggesting videos that users may want to watch based [...]
What sort of information retrieval system would you build if you knew that all the users of your system would be expert or highly-motivated amateur searchers? What sort of system would you build when you have a very large collection of unstructured information, and the goal in searching that information is not to find one document (e.g. navigate to a home page), but to find (a) relationships between documents, or (b) large sets of documents that all pertain to a single topic? How would your algorithms be different? How would your interfaces be difference? How would the process itself (that middle layer in between algorithms and interfaces) be different?
Via Daniel Tunkelang’s recent post, I think that Government information might be a perfect domain in which to ask (and answer) these sorts of questions. The U.S. Open Government Initiative has as its goal the release of loads of raw government data for use by any individual or organization. How are people going to use this data? What types of questions will they ask? What types of questions could they ask, if given the proper tools (i.e. what might they not know that they want to ask, until it becomes possible?)
Two types of information retrieval might be perfect for this domain: Exploratory Search and (Explicitly) Collaborative Search. Continue reading…
Via Xavier Amatriain: The Dirty Little Secret About the “Wisdom of the Crowds” – There is No Crowd:
This is hardly the first time that the so-called “wisdom of the crowds” has been called into question. The term, which implies that a diverse collection of individuals makes more accurate decisions and predications than individuals or even experts, has been used in the past to describe how everything from Wikipedia to user-generated news sites like Digg.com offer better services than anything created by a smaller group could do.
Of course, we now know that simply isn’t true. For one thing, Wikipedia isn’t written and edited by the “crowd” at all. In fact, 1% of Wikipedia users are responsible for half of the site’s edits. Even Wikipedia’s founder, Jimmy Wales, has been quoted as saying that the site is really written by a community, “a dedicated group of a few hundred volunteers.”
<snip>
Still, there [has] yet to be a perfect solution to the problem. Perhaps it’s time we give up the idea that the “wisdom of the crowds” was ever a driving force behind any socialized, user-generated anything and realize that, just like in life, there will always be active participants as well as the passive passerbys.
I have never quite liked the notion of “wisdom of crowds”, and the hype behind it even less, so I”m glad to see signs that the hype cycle is finally starting to wind down. However, by having to confront exactly what it was that I didn’t like about the notion, I was intellectually forced to propose an alternative: Explicit Collaboration in Search. As I wrote half a year ago: Continue reading…
As a researcher, I have more questions than answers. And one of the questions that I have is in regards to the widely-accepted maxim that users are too lazy to give explicit relevance feedback to the search engine. See Danny Sullivan’s take, here.
Perhaps I am stuck back in a view of Information Retrieval that is 10-15 years old, but I tend to find my views heavily shaped or influenced by things like the following bit from Marti Hearst’s chapter in Modern Information Retrieval:
An important part of the information access process is query reformulation, and a proven effective technique for query reformulation is relevance feedback. In its original form, relevance feedback refers to an interaction cycle in which the user selects a small set of documents that appear to be relevant to the query, and the system then uses features derived from these selected relevant documents to revise the original query. This revised query is then executed and a new set of documents is returned. Documents from the original set can appear in the new results list, although they are likely to appear in a different rank order. Relevance feedback in its original form has been shown to be an effective mechanism for improving retrieval results in a variety of studies and settings [salton90a][harman92c][buckley94b]. In recent years the scope of ideas that can be classified under this term has widened greatly.
Given that explicit relevance feedback works, why is it essentially non-existent on the web? A bird in the hand (an explicit relevance judgment) is worth two in the bush (two implied or inferred relevance judgments). Continue reading…
As a researcher, it is occasionally quite interesting to reread thoughts and positions that I’ve taken in years and works past. Sometimes I can observe a marked shift from my previous thinking; avenues or approaches that I once considered fruitful I now no longer do. And sometimes I can observe hints and seeds of my current research; avenues of which I only had a vague inkling have blossomed into larger pursuits.
In April of 2006 I had the good fortune to attend a Dagstuhl Seminar on Content-Based Multimedia Information Retrieval (I am toward the upper left corner of the seminar group photo). Ramesh Jain has a good writeup of Dagstuhl Seminars, what they are and how they work. In the abstract of my Seminar presentation I wrote:
Continue reading…
Now seems as good a time as any to post a quick recap of the series of collaborative information seeking posts that Gene and I have been writing over on Palblog. We’re about halfway through the series.
Communicating about Collaboration Communicating about Collaboration: Intent Communicating about Collaboration: Synchronization Social Search Social Search Redux
I [...]
There is an an interesting comment thread happening over on the FXPAL blog, about the differences between social search and collaborative search:
http://palblog.fxpal.com/?p=350#comments
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