7FDUPS'PSHF#FODINBSL 4VNNBSZBOEGVUVSFQMBOT "HFOEB Ref: Rayleigh: A Management Platform for Ray clusters https://tech-verse.lycorp.co.jp/2025/ja/session/1022/
extraction Retrieval model Ranker model raw features raw + processed features item candidates end users Heuristic post-process Vector Forge focuses this area
: [-6.7, 4.9, ..., 3.8] ... 6TFS*UFN EBUBGPS-*/& 4UJDLFS 6TFS EFNPHSBQIZ -*/&4UJDLFS user_1 : [ 4.8, 9.9, ..., -9.7] ... 6TFS EFNPHSBQIZ user_1 : [-8.4, -3.8, ..., -4.7] ... Each feature extraction model produces a different vector for each user. Training frequency Every week Prediction frequency Every day Feature extraction models
BSDIJUFDUVSFXJUIBEEFEVTFSWFDUPSGFBUVSFT CVUXJUIPVUJUFNWFDUPS GFBUVSFT User tower Item tower List of viewed items Target items User vector Item vector Contrastive loss 6TFSWFDUPSGPS -*/&0GGJDJBM "DDPVOU 6TFSWFDUPSGPS -*/&"ET User/item vectors from retrieval model are different from user/item vectors from feature extraction Item embedding User features
*UFNWFDUPSGSPN $'NPEFM *UFNEBUB UFYUEBUB Text BERT Item text data Predicted item vector Text BERT model produces item vector from text content 1 https://arxiv.org/pdf/1611.00384
EBUB Content model inherently needs less frequent training because meanings of text/image don’t change overnight. *UFNEBUB ID-based model CB2CF model 6TFSWFDUPS *UFNWFDUPS *%CBTFE *UFNWFDUPS $#$' 6TFSMPH EBUB ID-based → CB2CF training dependency *UFNEBUB As is New: Vector Forge
/FXWFDUPSGFBUVSFT single vector space text vector space vision vector space 6TFSNPEFM *%CBTFE *UFNNPEFM *%CBTFE *UFNNPEFM 4-.--.#&3 5 *UFNNPEFM $-*1 6TFSNPEFM 7FDUPS 'PSHF *UFNNPEFM $#$'