Setting up a classical DLRM at work, show of hands: who has managed to bridge the gap to generative recommenders or LRMs?
Our survey identified quite a substantial list of challenges: product cardinality (Semantic IDs?), low performance vs discriminative recommenders (our user intents are heavily biases towards repetitive products), cold starts, entity features, missing explainability suite, training cost, prediction cost, etc.
Has anyone done it easily? Where is the tutorial for generative recommenders ?
GR expertise is essentially only in-house at major social media companies or people hired from these companies. Closest thing to a tutorial for LRM is the Kuaishou onerec papers.
Setting up a classical DLRM at work, show of hands: who has managed to bridge the gap to generative recommenders or LRMs?
Our survey identified quite a substantial list of challenges: product cardinality (Semantic IDs?), low performance vs discriminative recommenders (our user intents are heavily biases towards repetitive products), cold starts, entity features, missing explainability suite, training cost, prediction cost, etc.
Has anyone done it easily? Where is the tutorial for generative recommenders ?
GR expertise is essentially only in-house at major social media companies or people hired from these companies. Closest thing to a tutorial for LRM is the Kuaishou onerec papers.
So bytedance has the luxury of building custom storage for this, meanwhile we're over here trying to shoehorn user sequences into redis sorted sets