Relevancy Experimentation Framework

[obulat]

Summary

Develop an experimental framework for assessing how relevancy is affected (either positively or negatively) by changes made to our search algorithms and the data itself.

Description

The search relevancy sandbox project outlines the infrastructure pieces necessary for making rapid changes to our search algorithms and components. The next step for us in order to leverage this toolbox is to develop an experimentation framework for assessing how relevancy changes when certain changes are made. This could be done in two parts:

• A hybrid automation + manual process which takes snapshots of specific result sets in staging, then prompts a maintainer to compare the relevancy between the two snapshots. This is intended to be a blunt approach; do the results still feel relevant is all that's necessary, if the results are wildly irrelevant where they previously were not, then we do not roll the projected change out to production.

• An automated assessment of relevancy based on A/B experimentation and paired with the results from our analytics. This would use metrics like SELECT_SEARCH_RESULT, LOAD_MORE_RESULTS, and any new metrics we require to assess if users are clicking through results at a higher/lower rate given any changes.

The former would act as more of a "gut check" with little nuance, and the second would serve as a longer-running assessment of result relevancy based on actual user behavior.

Best guess at list of implementation plans:

• Develop algorithm for assessing relevancy given analytics
• Staging snapshot automation & manual assessment protocols
• Infrastructure changes necessary for performing A/B experimentation
• Framework for combining relevancy assessment metric and infrastructure changes to accurately run an experiment

Documents

• Project Proposal
• Implementation Plan

Issues

Prior Art

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