Yesterday I had the pleasure to attend a lecture at UCLA given by Peter Ingwersen, Professor of Information Retrieval and Seeking, Royal School of Library and Information Science, Denmark. Peter was the 2009/2010 recipient of the Contribution to Information Science & Technology Award from the Los Angeles chapter of ASIS&T, the 21st person to be so honored.  He gave an interesting talk on frameworks in information seeking which explored the philosophical foundations of the “Cranfield paradigm” and proposed ways of extending the approach to incorporate the behaviors of–gasp!–real users!

Drawing on material from his book (co-written with Kalervo Järvelin) “The Turn: Integration of Information Seeking and Retrieval in Context“, he described his “Spaceship” model

A general analytical model of information seeking and retrieval (from Information Research Vol. 10 No. 1, October 2004)

Professor of Information Retrieval and
Seeking, Royal School of Library and Information Science, Denmark

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Some time ago I wrote about reports of books being replaced by electronic devices for academic reading. My take was that this kind of techno-utopianism will not improve the quality of education because the current crop of devices is not designed for active reading. This hypothesis was put the test recently at Princeton and four other universities. At Princeton, 53 students in three courses participated in an experiment where they were asked to use a Kindle device for course-work related reading. Results reported by the Daily Princetonian indicate that while the amount of in-course printing dropped by about 50%, students complained about a variety of limitations of using these devices for course work. Not surprisingly,

…users said they often found its design ill-suited for class readings. Students and faculty participating in the program said it was difficult to highlight and annotate PDF files and to use the folder structure intended to organize documents, according to University surveys. The inability to quickly navigate between documents and view two or more documents at the same time also frustrated users.

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In the spirit of Many Eyes, Jeff Clark has been developing visualizations of various kinds, including those of various Twitter collections. For example, his Twitter Venn diagram looks at intersections of tweets with three user-specified terms to help understand something about the way different concepts co-occur. Other visualizations look at word distributions associated with pairs of terms, and term use timlines.

The graphs are pretty and, perhaps, informative. His goal is to visualize complex data that don’t lend themselves to standard bar and pie charts. When these visualizations are effective, they can reveal insight that textual representations fail to convey, but the trick is to understand what is effective when. Tufte‘s design guidelines are a start, but one based on a rather static notion of data visualization. Apparently Bertin was more attuned to interaction, but was still trapped in a static medium.

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Hillary Mason of bit.ly wrote a nice summary of some key issues raised in the recent Search in Social Media 2010 workshop. (For other commentary, see Daniel Tunkelang”s post and our pre-workshop comments.) Hillary asked several important questions, that break out into two main topics: what and how can we compute from social data on one hand, and what are the implications of those computations. Aspects such as computing relevance, how to architect social search engines, and how to represent users’ information needs in appropriate ways all represent the what and how category. We can be sure that adequate  engineering solutions will be found these problems.

The second topic, however, is more problematic because it deals more with the impact that technology has on the individual and on society, rather than about technology per se.

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Last week I saw a BayCHI talk by Elaine Wherry of Meebo, in which she used the history of classical music as an analogy for the evolution of user interface and interaction design of web-based user interfaces. The parallels she established between the Baroque era and what we have experienced in the last decade of “Web 2.0” interfaces are compelling.

Baroque music arose during the Renaissance as a reaction to the impoverished musical forms characteristic of the middle ages, an era dominated by monastic chants and songs of Troubadours. The Renaissance brought a revolution in music-making technology with the invention of a range of musical instruments. Unlike the sparse music that preceded it,  Baroque music is characterized by a profusion of notes that, while initially interesting, tend to overwhelm and can render the composer’s melody unrecognizable.

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I wanted to clarify the point I tried to make in my blog post about Bobo and LinkedIn’s use of faceted search. I ended that post with a confusing question about faceted search framework in Lucene, and was quickly reminded by Bob Carpenter and others that Solr provides that capability. My comment was poorly made.

My comment about facets was related to programming the interface rather than to retrieval algorithms. It seems to me that a good, interactive interface for faceted browsing is every bit as complicated to build as a  good engine for finding the facets in the first place. Lately I’ve been messing around with Javascript programming, and am getting frustrated by the seemingly unnecessary complexity of building web user interfaces that are both efficient and effective.

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Last week Jeremy and I attended the SSM2010 workshop held in conjunction with WSDM2010. In addition to chairing one of the panels, I got an opportunity to demonstrate an interface that I built to browse Twitter search results, to which Daniel alluded in his summary of the workshop. The system is described in a position paper (co-authored with Miles Efron) that has been accepted to the Microblogging workshop held in conjunction with CHI 2010.

The idea behind this interface is that Twitter displays its search results only by date, thereby making it difficult to understand anything about the result set other than what the last few tweets were. But tweets are structurally rich, including such metadata as the identity of the tweeter, possible threaded conversation, mentioned documents, etc. The system we built is an attempt to explore the possibilities of how to bring HCIR techniques to this task.

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For an article I’m writing for a well-known magazine I needed to get my hands on one of the new iPads for a few moments, pre-release. I went bottom-up, top-down, pretended to be a reporter, employed vague threats, etc. All to no avail. I suppose the powers-that-be have a good reason for this, but it is a mystery to me. I mean at this point, the cat is out of the bag! On the other hand, I’m not really in the target market (like these guys, I find Apple’s mobile devices far too restrictive — my particular pet peeve is having to subvert the OS just to mount as a drive). So maybe I’m not meant to understand.

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Last Wednesday Jeremy and I participated in the SSM2010 workshop organized by Ian Soboroff (NIST), Eugene Agichtein (Emory University), Daniel Tunkelang (Google), and Marti Hearst (University of California, Berkeley).  It was a full day of panels, discussions and poster presentations on a variety of topics related to search, to social media, and how to combine the two. In an earlier post, I wrote about one way that we can characterize the space, and Daniel did an excellent job of summarizing the workshop, which was also cross-posted  at BLOG@CACM.

I am still trying to digest all that I learned during the day, but I can say that one of the challenges was live-tweeting the event. I was one of several people who tweeted about what was happening in the panels and about the issues that were raised. Over 500 tweets were sent and resent with the workshop’s hashtag by people at the event and elsewhere. It was interesting to see other people pick up some of the topics and comment on them. In particular, several of my twitter friends who are not part of the SSM research community had commented on the tweets, and retweeted certain aspects of the discussion.

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Dave Bacon, who wrote the elegant overview of the research discussed in my New Year’s Day post, just published his review, joint with Wim van Dam, of Recent Progress in Quantum Algorithms. Bacon writes beautifully, and this piece is no exception.

Most people have heard of no more than two quantum algorithms: Shor’s factoring algorithm and Grover’s search algorithm. For five years after Grover’s algorithm, no one discovered a significantly novel quantum algorithm, only variations on Shor’s and Grover’s algorithms were found. The first truly new quantum algorithms were discovered starting in 2001. Now there are many quantum algorithms found using a variety of approaches, though the applications remain restricted.  My recent overview of quantum computing mentions many of these algorithms. Bacon and van Dam provide a more detailed, but still high level, view of these algorithms. They group the algorithms into four categories corresponding to different approaches: quantum random walks, wave packet scattering, finding hidden symmetries, and simulating quantum physics. I hope many of you will enjoy learning more about, in their words, “the benefits of … studying the notion of an algorithm through the perspective of the physical laws of the universe.”