I recently read on Jeff Dalton’s blog that a new open-source search engine, called  Ivory, has been released by Jimmy Lin. Ivory is based on Hadoop, and is  designed to handle terabyte-sized collections. Unlike Lucene, this is a research project, Jimmy Lin writes,

aimed at information retrieval researchers who need access to low-level data structures and who generally know their way around retrieval algorithms. As a result, a lot of “niceties” are simply missing—for example, fancy interfaces or ingestion support for different file types. It goes without saying that Ivory is a bit rough around the edges, but our philosophy is to release early and release often. In short, Ivory is experimental!

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In her talk at the IR Eval workshop at SIGIR 09, Sue Dumais called for an experimental platform for conducting research in information seeking (thanks Sakai-san!). She called it a Living Laboratory. This is a tremendous idea, the high tide that lifts all boats. Whether you’re interested in doing log analysis, interface design evaluation, building new indexing algorithms, or other kinds of research, having real data sets with real users and real information needs can move the field forward in ways that Cranfield-style experiments do not.

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Faceted search interfaces for metadata-rich datasets such as product information have been around for a while. e-Bay and Amazon are two obvious examples. Faceted search for textual data is only slowly making its way into the commercial realm (see NewsSift, for example) but have been receiving increasing attention in research. Villa et al. presented an interesting paper at SIGIR09 in which they compared different interface layouts for handling aspects, and compared the effectiveness of aspectual search with a conventional interface for different tasks.

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Diane Kelly presented an interesting (and much tweeted-about) paper at SIGIR this week. The paper, “A Comparsion of Query and Term Suggestion Features for Interactive Searching,” co-written with Karl Gyllstrom and Earl Bailey, looks at the effects that query and term suggestions have on users’ performance and preferences. These are important topics for interactive information seeking, both for known-item and exploratory search.

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At CHI 2007, in a workshop on exploratory search, we had a long discussion of the definition of exploratory search, during which Sue Dumais kept challenging the room to look broadly, bringing in examples and counter-examples not only from full text search, but from more structured datasets that were also fair game.

Exploratory search is just one part of HCIR; her work on adapting systems to users’ vocabulary (not vice-versa) that led to LSI, innovative search interfaces (“If in 10 years we are still using a rectangular box and a list of results, I should be fired.” ), finding and re-finding information on your personal computer, and personalization of search results all fit squarely into the HCIR space.

Those who attended the HCIR’08 workshop organized by Daniel Tunkelang (Endeca), Ryen White (MSR), and Bill Kules (CUA) got a great overview of Sue’s research. This week, during her  opening keynote at SIGIR (see notes from Jeff Dalton and Jonathan Elsas, who, unlike me, were actually there!) Sue described the course of her career as an IR researcher, first at Bellcore and at Microsoft Research. In her career, she has consistently focused on the user both for inspiration for design, and for evaluating the systems.

“If you have an operational system and you don’t use what your users are doing to improve, you should have your head examined” (from  Jeff Dalton)

I expect we’ll be seeing more interesting and innovative results from her group, both at SIGIR and at the HCIR workshop series.

Alex Iskold recently wrote on the ReadWriteWeb about potential improvements in search that could be derived from incorporating evidence from one social network to affect the ranking of documents. The idea is that people you know, people with similar interests, friends-of-friends, authorities, and “the crowd” could all contribute to change the ranking on documents that a search engine delivers to you because the opinions or interests of all these people can provide some information to help disambiguate queries.

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Thanks to Nathan Treloar’s post on search user experience, I came across a set of Flickr pages created by Peter Morville dedicated to documenting user interface designs for search interfaces. His goal is “collecting search examples, patterns, and anti-patterns” to inform subsequent design. The pages collect many images of well-designed sites, grouped into about 20 different categories (e.g., Faceted Navigation, Pagination, Clustering, E-Commerce, etc.) with annotations by Peter highlighting some important aspects of each design.

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Yahoo! Search Pad was released last week without much ado, certainly not to the kind of media buzz surrounding Google and MS announcements. Search Pad collects documents you click on in search results, and allows you to annotate them with notes. The interface, while simple, is not necessarily easy to figure out. It took me some time to poke around and figure out how it works. It some ways, it is similar to Bing’s history mechanism. It’s more useful than the history mechanism because it allows the user to type notes or copy pieces of documents into the Search Pad to help with document triage and other recall-oriented aspects of exploratory search. On the other hand, the history mechanism works in a more intuitive way, and keeps track of documents you’ve already seen when you re-visit a query.

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I dodged being empaneled on a jury, and I made it to IBM Almaden to attend most of NPUC. I did miss the talk by Brad Myers, which I’ll have to view on video, but got to see most of the other presentations and the poster/demo session. One demo I found particularly interesting was Mira Dontcheva‘s Blueprint work. Blueprint is an Eclipse plugin for Flex programming that allows people to search for snippets of code directly from the IDE, and displays them in an overlay or side bar. Blueprint makes it possible to search the web with an interface similar to the typical auto-complete functionality. Furthermore, because it understands Flex syntax, its ranking should more accurate than a regular full-text index that happens to contain code. When you select a search result, Blueprint inserts it into your code, and automatically annotates the code to include the URL at which the snippet was found so that you can re-visit that page later.

Adobe Blueprint screenshot

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In his comment to an earlier post, Miles Efron reiterated the usefulness of the various TREC competitions to fostering IR research. I agree with him (and with others) that TREC has certainly been a good incubator both in its annual competition and in follow-on studies that use its data in other ways.  And, as Miles points out,we have seen a proliferation of collections: everything from the original newspaper articles to blogs, video, large corpora, etc.

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