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search safety net

Search safety net

The search safety net is a novel feature to help improve searching; helping users not miss important papers.  I wanted to explain it – simply – but have failed on that score.  It’s important so I hope you can make sense of what I’ve written.  If you have any questions, my email is jon.brassey@tripdatabase.com.

After a search you will see a new ‘Search Safety Net’ button

If you click that it’ll bring up a list of related search terms.  It does this by looking at the top 250 search results and analysing the search terms people have previously used when clicking on these results.  This works on the notion that a single document can be clicked on after numerous searches.  For instance, in the example above search terms might have been ‘prostate cancer screening’, ‘MRI screening’ etc.

The next section of the search safety net happens AFTER you’re conducted your search and found a number of documents you like AND looked at (or simply clicked the ‘check box’ to the left of the result).  If you click on the Search Safety Net button again you see three columns of results:

The first column is closely related articles, the second is other related articles and the third is the related search terms.  The latter column is similar to the description of related search terms above, but is based purely on the documents clicked (as opposed to the top 250 results).  However, to understand the process behind the other two columns you need to understand a clickstream data.

Paper 1 ———- Paper 2 ———- Paper 3

In the above there are three papers (1-3).  A user, in the same session, clicks on Paper 1 and Paper 2, therefore we can make a link between the two.  Another user might click on Paper 2 and Paper 3, again making a link.  So, Paper 1 is connected to Paper 2 (a single step, using network language) while Paper 3 is two-steps away from Paper 1.  We have this data for all articles in Trip.

Slightly simplifying things (!) the first column is the most popular related articles based on documents that are one step away from the documents clicked.  So, we look at all the articles clicked by the user and pull back all the documents that are one step away, displaying most ‘popular’ at the top.  The second column are all the documents that are two steps away.  This is likely to find less focused results, but the occasional really interesting study that might have been missed.

Two important issues:

  • For this to work requires clicks, if the documents you’ve looked at has no clicks, then you’ll get no results.
  • This is being released as a ‘beta’ bit of software, as in we’re still developing it.  At present it is available to both free and Premium users of Trip.  However, this is likely to change in the near future.

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Search safety net (or ‘what have I missed?’)

We’re continuing to discover uses for the clickstream data Trip has and the new use is the search safety net – which we’re currently testing.

The idea is that as you search Trip we note which articles you’ve clicked on and then, using clickstream data, predict other articles that might be related, or those you might have missed (hence the name). See the video embedded below.

There are two issues/problems/challenges:

  1. Lack of data. This only works on clickstream data, so it requires clicks!  Very new articles or obscure articles will not have the data.  As it happens, in the tests we’ve done, it’s been – broadly – really good.  But when we roll it out, it’s something to consider.  
  2. User experience.  This is the biggest challenge is how will users interact with the ‘service’?  In other words where do we put the results?  Do we automatically show them somewhere on the results page?  If so, will that annoy people who don’t want the service?  Alternatively, we create some sort of ‘search safety net’ button which would require a user to click the button.  This means many people will simply not see it and miss out.

Once we solve the user interaction side of things, we’ll roll it out.  In the interim, if you want to give it a try (and be one of the first people to use it) then drop me a line (jon.brassey@tripdatabase.com).

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