Now that we have the feature deployed (behind a feature flag), and have an
initial "does it do anything?" test going out today, along with an upcoming
integration with our satisfaction metrics, we need to come up with how will
will try to further move the needle forward.
For reference these are our Q2 goals:
- Run A/B test for a feature that:
- Uses a library to detect the language of a user's search query.
- Adjusts results to match that language.
- Determine from A/B test results whether this feature is fit to push to
production, with the aim to:
- Improve search user satisfaction by 10% (from 15% to 16.5%).
- Reduce zero results rate for non-automata search queries by 10%.
We brainstormed a number of possibilities here:
https://etherpad.wikimedia.org/p/LanguageSupportBrainstorming
We now need to decide which of these ideas we should prioritize. We might
want to take into consideration which of these can be pre-tested with our
relevancy lab work, such that we can prefer to work on things we think will
move the needle the most. I'm really not sure which of these to push
forward on, so let us know which you think can have the most impact, or
where the expected impact could be measured with relevancy lab with minimal
work.
_______________________________________________
discovery mailing list
[email protected]
https://lists.wikimedia.org/mailman/listinfo/discovery
