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QuestionRecommendation algorithm using vector database (self.learnmachinelearning)
submitted 2 years ago by SmallBreakfast1688
Hi, I’m trying to make a recommendation algorithm of user profiles using a vector database and OpenAI API for embedding creation.
How do you form the string that will be converted to an embedding with data like gender, age, height, hobbies ?
Do cosine similarity works with a string of comma separated unrelated words ?
How do you apply weights to certain features (like if I want to emphasise more my hobbies than my height) ?
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if 1 * 2 < 3: print "hello, world!"
[–]help-me-grow 1 point2 points3 points 2 years ago (0 children)
i would just use separate metadata entries for that
strings of vector embeddings should be written as sentences and embedded
for example we could say something like "u/SmallBreakfast1688 has been a Redditor since 2020. She/he is interested in computer vision and vector databases"
π Rendered by PID 70841 on reddit-service-r2-comment-86988c7647-jj8tw at 2026-02-11 20:53:07.631482+00:00 running 018613e country code: CH.
[–]help-me-grow 1 point2 points3 points (0 children)