When users interact with Trip we capture what they’re doing – the search terms, articles clicked etc. Previously I have shown how we can map this data using this stored (clickstream data). Below is a map of articles relating to urinary tract infection (UTI):
You can see, from the annotation, that similar articles cluster (bottom left is a cluster of articles on UTI and cranberry). To better understand how we create these graphs see these two articles:
I’ve been working with this data for a while and uses keep appearing. One that is very attractive is in improving search results. For the sake of argument let’s say the articles in the image above (indicated by individual nodes in the image above) are evenly spread in the top 2-3 pages of search results in Trip.
As soon as a users makes their first click they are telling us where they are, in relation to their interest/intention, in the map of articles (see below):
Using the above example a user clicks on an article in the bottom left of the image (in a cluster of articles on UTI and cranberry) the chances are they are likely to be interested in others articles that are close by (1-2 ‘steps’ away). This works on the same principle as normal maps – if you’re looking at a street map of New York and you’re looking at a particular road in, say, Brooklyn it’s likely that your immediate interest is in the area close by to that road as opposed to say the Mission in San Francisco.
So, could we create a system that can allow users to re-order results as soon as they click on their first result? Could we do this dynamically (no clicking)? The principals seems sensible but as with most of these things it’s how to operationalise them that’s the key…!