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Posts Tagged ‘Information visualization’

I am delighted to announce publication of our latest open-access scholarly paper ‘Rethinking ‘Advanced Search’: A New Approach to Complex Query Formulation‘, which has just been published in the proceedings of the 41st European Conference on Information Retrieval (ECIR) in Cologne. This work is a collaboration with Jon Chamberlain and Udo Kruschwitz of Essex University, and accompanies our demo at the event.

The paper focuses on the application of query visualisation to structured searching and in particular the challenges associated with the recruitment profession, and hopefully complements some of the more opinion or design-oriented posts on this site. I’ve appended the abstract below. For free access to a copy, visit the ECIR website.

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I am delighted to announce publication of our latest open-access scholarly paper ‘A Visual Approach to Query Formulation for Systematic Search‘, which has just been published in the proceedings of the 4th ACM SIGIR Conference on Human Information Interaction and Retrieval. This work is a collaboration with Jon Chamberlain of Essex University and Farhad Shokraneh of Nottingham University, and accompanies our demo at the event.

The paper focuses on the application of query visualisation to healthcare information, and hopefully complements some of the more opinion or design-oriented posts on this site. I’ve appended the abstract below. For free access to a copy, visit the ACM Digital Library.

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textmining

I received a pleasant surprise in the post today: my personal copy of Text Mining and Visualization: Case Studies Using Open-Source Tools, edited by Markus Hofmann and Andrew Chisholm. Now I don’t normally blog about books, since as editor of Informer there was a time when I would be sent all manner of titles for inspection and review. But I’ll make an exception here. This is partially since Chapter 7 is my own contribution (on mining search logs), as discussed in my earlier blog posts. This is complemented by 11 other chapters, covering a variety of topics organised into four sections:

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A little while ago I posted a piece examining some of the shortcomings in the way that search strategies are currently expressed; arguing that the approach essentially hasn’t changed in decades. Moreover, it is predicated on a rather primitive notation that owes much to first generation basic, relying on arbitrary concepts such as line numbers to convey structure and organisation.

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Just a quick reminder that next Friday (1st August) is the deadline for submissions to EuroHCIR 2014, which I am co-organising with Max Wilson, Birger Larsen, Preben Hansen and Kristian Norling. This is the fourth year we’ve run the event, so we’re hoping for a good set of submissions to keep up momentum. As before, we’re accepting both research and practice-oriented papers, so if you have any queries (particularly about the latter) just drop me a line.

The event itself is on 13 September at BCS London, with a poster session/social scheduled for the evening before. I’ve appended a summary of the call for papers below, and further details can be found at the EuroHCIR 2014 website. Hope to see you there!

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Expectation Maximization applied to a new sample of 100,000 sessions

In a previous post I discussed some initial investigations into the use of unsupervised learning techniques (i.e. clustering) to identify usage patterns in web search logs. As you may recall, we had some initial success in finding interesting patterns of user behaviour in the AOL log, but when we tried to extend this and replicate a previous study of the Excite log, things started to go somewhat awry. In this post, we investigate these issues, present the results of a revised procedure, and reflect on what they tell us about searcher behaviour.

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EM, 7 features

As I mentioned in a previous post I’ve recently been looking into the challenges of search log analysis and in particular the prospects for deriving a ‘taxonomy of search sessions’. The idea is that if we can find distinct, repeatable patterns of behaviour in search logs then we can use these to better understand user needs and therefore deliver a more effective user experience.

We’re not the first to attempt this of course – in fact the whole area of search log analysis has an academic literature which extends back at least a couple of decades. And it is quite topical right now, with both ElasticSearch and LucidWorks releasing their own logfile analysis tools (ELK and SiLK respectively). So in this post I’ll be discussing some of the challenges in our own work and sharing some of the initial findings.

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