The Trip algorithm is great. To explain, the algorithm is the ‘behind the scenes’ way we order the results you see on the screen. As mentioned, it works great.
However, that’s not to mean it can’t be improved and we are currently working with a number of academics to try to use our data to improve search methods generally (not just Trip). We have an accompanying paper TripClick: The Log Files of a Large Health Web Search Engine. The idea is that, by using our clickstream data (what people search for, what they click on etc), machine learning techniques can be used to improve search results.
What’s particularly exciting is that we have created a competition, pitting different academic centres against each other, to see who returns the best results. Yesterday we had our first academic centre to report results:
- The improvement over baseline was large
- It was from a team headed by Prof Allan Hanbury at TU Wein, the wonderful lead of Trip’s Horizon 2020 work a few years back.
The competition is likely to run for months and after that it’s a question of taking stock and seeing how we can utilise the techniques within Trip.
If we can improve on our search results, even marginally, it’ll be a great result.
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