So, my work in Glasgow has been around exploring these issues and is starting to produce results and we’ll shortly be moving to a testing phase (so we will be asking for volunteers). In short, there are three main areas of our work.
Anonymous users (those not signed in). We’re planning on testing a technique called search results diversification. Currently, when you issue a query to Trip, our search algorithm makes no attempt to factor-in intentions. For a given search it returns results based on their text, quality and date. If all of the documents focus on one specific intent, then that’s too bad. So, a user might search for breast cancer and it might be that most of the results focus on screening. If the person’s intention wasn’t related to screening the results are bad. So, to overcome this you can use diversification. This involves looking back at previous searches to estimate likely intentions. For instance, if someone searches for DVT, what words are subsequently added to modify the search, to make it more focussed? In this example the top five search reformulations are:
- dvt treatment
- dvt prophylaxis guidelines
- dvt d-dimer
- dvt cancer
- dvt diagnosis
Known user (logged in). This is where it gets particularly interesting! We have information on the user from their registration e.g. their profession, country and clinical areas of interest. We also have other information based on their search history. In effect, we build up a search profile. With a search profile we can find similar users – the measure we’re calling ‘Clinicians Like Me’. How does this help? In a couple of ways:
- When someone searches we can use the diversification technique but instead of diversification across all searches conducted in Trip, it’ll be diversification based on the user themselves and clinician’s like them. So, if you’re a oncologist the diversifications are likely to be different from a general population. Using the first example, pain, and you’re an oncologist the diversifications will be based on how other oncologists have reformulated the search – making the results much more focussed.
- Boosting the scores of individual documents clicked on by similar users. So, in the normal Trip search we might see oncologists are typically clicking on results 8 and 15 for a given search. Using this observation, we can subsequently boost these results for other oncologists when they next search.
Highlighting new research. We believe that there are ways that we can more effectively highlight newly published research to our users. We currently add around 5,000 new documents a month to Trip (500 secondary reviews and the bulk of the rest is from primary research), but our current email update approach is not well tailored to the breadth and diversity of our user base. However, using the above techniques (learning from the users and similar users) we can start to make predictions as to their future information needs and hence better find the new documents you want to see.
All the above sounds wonderful, even magical. But, Trip is about being evidence-based, so we need to generate evidence that this approach is worthwhile adopting. To do this we need volunteers (health professionals only - sorry everyone else). The above approach could dramatically improve the already wonderful Trip search. So, if you’re a clinician please contact me (firstname.lastname@example.org) and I can tell you what’s involved – it really won’t be too onerous.