Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

ArXi:2604.18351v1 Announce Type: cross Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models. At industrial scale, embedding tables constituted by such vectors of all users/items demand a vast amount of parameters and impose heavy compute and memory overhead during