Recommender System Fragment 1
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Procedure
- when a scenario(user) comes, fetch all the user’s properties and then pull candidates(items) from several queues depending on recall strategy(how? what model?). rank over all these candidate(~1000?) and push out the top ones.
Details
- each queue is updated dynamically.
- queue length? ~ 1000?
- always fetch from queue front? a bit slower and slower for bloom filter but acceptable cz queue not too long.
- ranking is a pre-trained stable model. just do ‘prediction’. parallel if needed.
- all queue and BF could be in memory.
Qs
- how to get user’s features? kv db and/or what? – kv db
- how to get doc features? are they stored in queue? – yes
- how and where to apply anti-spam & anti-cheating module?
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