
Tinkering and building. Technical SEO with @Glassdoor.
Forked River, NJ
Joined August 2013
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Replying to @VeryWellVersed @iPullRank
Sifting through data looking for the silver bullet that some random KPI demands
No models, no massive compute, and no heavy pretraining. The math is also simple enough it could be done in any program. I look forward to more ingenuity in the cost-effective SEO + ML space! ✌️
The untagged keyword with the shortest distance to a tagged keyword shares the same tag. The code looks complicated, but the results are simple and cheap.
Using the paper, I can start with a training set of keywords that are tagged. Tech and Automotive. Then I have keywords that I want to be tagged for me. For each keyword I want tagged, I add it together with a training keyword and see what the distance is between compressed items
Last week a paper was released showing how gzip could be used for an embedding/compression step. It makes perfect sense when you see how gzip works. aclanthology.org/2023.findin…








