Exploratory search often takes place over time. Searchers may run multiple queries to understand the collection, to refine their information needs, or to explore various aspects of the topic of interest. Many web search engines keep a history of a user’s actions: Bing makes that history readily available for backtracking, and all major search engines presumably use the click-through history of search results to affect subsequent searches. Yahoo Search Pad diagnoses exploratory search situations and switches to a more elaborate note-taking mode to help users manage the found information.

But none of these approaches makes it easy for a searcher to manage an on-going exploratory search. So what could be done differently? We explore this topic in a paper we’ll be presenting at the IIiX 2010 conference this August. Our paper reviews the literature on session-based search, and proposes a framework for designing interactions around information seeking. This framework uses the structure of the process of exploratory search to help searchers reflect on their actions and on the retrieved results. It treats queries, terms, metadata, documents, sets of queries, and sets of documents as first-class objects that the user can manipulate, and describes how information seeking context can be preserved across these transitions.

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The importance of understanding people’s activity to inform design is one of the central tenets of HCI. When design is grounded in actual work practice, it is much more likely to produce artifacts that fit with the way people work and the way they think. One key challenge when studying people for the purpose of informing design is to understand what aspects of existing work practice are essential to the work and what aspects are side-effects of existing technology (or lack thereof) and are fair game for innovation.

While HCIR research often relies on recall and precision measures to compare systems, qualitative methods are used as well. For example, Vakkari and his colleagues studied several students performing research for their Master’s thesis work. Researchers used a variety of techniques including diary entries and interviews to assess the evolution of searchers’ behavior over the course of a few months. Their findings led them to fill in some of the details of Kuhlthau’s model of information seeking.

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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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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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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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This is one in a series of posts advertising internship positions at FXPAL for the summer of 2010. A listing of all internship positions currently posted is available here.

The focus of Human-Computer Information Retrieval (HCIR) is to help people find and make sense of the information that satisfies their evolving information needs, and to do so with an emphasis on interaction and not just on clever algorithms that attempt to approximate users’ intent. Over the past couple of years, we have developed some novel information retrieval algorithms such as collaborative search. While we have evaluated the work in various ways (e.g., evaluating algorithms offline and testing with people on artificial information needs), we have not tested them on people with real information needs.

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Last week I was in DC at the HCIR 2009 workshop organized by Bill Kules, Daniel Tunkelang, and Ryen White. This was the third workshop in the series, and by far the biggest and most diverse in terms of attendees. Proceedings are available online. Daniel and Max Wilson have already given pretty good coverage to what happened at the workshop, so I will focus on my impressions, starting with Ben Shneiderman‘s keynote.

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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.

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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