| Vvjjkkii renamed this task from "Explore using user clicks data to tune Wikidata search parameters" to "qqdaaaaaaa". Vvjjkkii raised the priority of this task from "Normal" to "High". Vvjjkkii removed a subscriber: Aklapper. Vvjjkkii added projects: CheckUser, Connected-Open-Heritage-Batch-uploads (RAÄ-KMB_1_2017-02), Tamil-Sites, Gamepress, Hashtags, JADE, KartoEditor, Language-2018-Apr-June, New-Editor-Experiences, Mail, TCB-Team. Vvjjkkii updated the task description. (Show Details) |
CHANGES TO TASK DESCRIPTION
Right now we are using tuning parameters for Wikidata search (both prefix and fulltext) which are more or less invented out of the thin air. I wonder if we could use some ML (or other) technology with actual user clicks data to have better tuning of those parameters.
Potential targets:
* Entity weight parameters (both satu params and weights of features on entities). We are only using incoming links and sitelinks counts now - maybe we should use more features?
* Relative weights of various matches - label, alias, description, other language, etc.?
* For fulltext possibly also more advanced features that we're building with Mjolnir?
The start would be to actually build a data pipeline allowing us to know which search result was chosen by the user, especially for prefix search which is used ~1M times a day.
As this is an exploratory task, suggestions about what else could be done here are welcome. 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
Potential targets:
* Entity weight parameters (both satu params and weights of features on entities). We are only using incoming links and sitelinks counts now - maybe we should use more features?
* Relative weights of various matches - label, alias, description, other language, etc.?
* For fulltext possibly also more advanced features that we're building with Mjolnir?
The start would be to actually build a data pipeline allowing us to know which search result was chosen by the user, especially for prefix search which is used ~1M times a day.
As this is an exploratory task, suggestions about what else could be done here are welcome. 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To: Vvjjkkii
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Cc: EBjune, Lazhar, gabriel-wmde, debt, Smalyshev, AndyTan, Zylc, 1978Gage2001, Lahi, Gq86, Darkminds3113, herron, pan199312, GoranSMilovanovic, Chicocvenancio, alanajjar, QZanden, Tbscho, LawExplorer, Lea_WMDE, Mattias_Ostmar-WMSE, Avner, JJMC89, Gehel, Jseddon, Ryuch, Mkdw, RuyP, JEumerus, FloNight, Trizek-WMF, KasiaWMDE, 0x010C, srodlund, Luke081515, grin, Bsadowski1, mys_721tx, Wikidata-bugs, Snowolf, aude, Huji, Gryllida, jayvdb, Tobi_WMDE_SW, revi, scfc, He7d3r, Romaine, Mbch331, Jay8g, Glaisher, Krenair, jeremyb, chasemp, Aklapper
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