Expected: We will see https://en.wikipedia.org/wiki/Chris_Gueffroy at the top of the result as it includes the exact search term (and is the only article with the exact search term)
Actual: This page is not within top 100 results and to find the page easily we need to add double quotes around it
@Aklapper I think this is slightly different.
technical notes: the mlr profile pushed the expected result around rank #160, the retrieval query ranks it at #30. This query was already showing bad ranking prior to mlr (not in top-10).
I don't think we include any phrase match feature in the mlr feature set, without digging too deeply in this case I don't see any other obvious feature that could help this particular query.
@EBernhardson would it be possible to include phrase match on text and/or text.plain as part of the feature evaluation you're running?
I can add a phrase match, but i'm not sure phrase match will be enough to push this single occurance all the way to the top. For this specific page a redirect containing the name might do the trick, but that's not as generalizable. Longer term I think named entity extraction has some potential here. A phrase match and a named entity match might (but needs to be evaluated) be enough.
I have a test model up that currently pushes Chris to the top: https://en.wikipedia.org/wiki/Special:Search?search=Ingo+Heinrich&fulltext=1&cirrusMLRModel=20180118-query_explorer-enwiki-v2
Edit: Updated model to one that is significantly less expensive, should be able to AB test this one after the next cluster restart.