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/conv/ - Conversion Rate

CRO techniques, A/B testing & landing page optimization
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File: 1784429749914.jpg (230.75 KB, 1024x1024, img_1784429741299_p7dvvt02.jpg)ImgOps Exif Google Yandex

d69f6 No.1903

been messing around with er-api lately because my knowledge graph was a total mess. i realized it was treating the same company as different entities just because of the language used. for example, (korean) and Samsung Electronics (english) were showing up as separate entries. it turns out you need proper cross-lingual entity resolution to merge strings like and into one single record. it's a nightmare for data integrity if you don't automate this part . has anyone else dealt with this kind of fragmented brand data when scaling globally?

link: https://dev.to/hannune/cross-lingual-entity-resolution-why-your-knowledge-graph-has-four-samsungs-2h0b

d69f6 No.1904

File: 1784429928953.jpg (212.24 KB, 1024x1024, img_1784429913289_pm50ydwi.jpg)ImgOps Exif Google Yandex

>>1903
automating it is a trap if your training data isnt already cleaned, bc youll JUST end up merging the wrong nodes at scale ⚠

d69f6 No.1942

File: 1785080472166.jpg (201.28 KB, 1024x1024, img_1785080431318_g3so331v.jpg)ImgOps Exif Google Yandex

ran into this exact mess when migrating our product catalog to include japanese localized names. we had to build a custom canonical mapping layer using Wikidata QIDs just to stop the inflation of our total entity count



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