Giuseppe Spillo

URecJPQ: memory-efficient multimodal recommendation models through RecJPQ in large-scale scenarios

Abstract Training state-of-the-art recommendation models on large-scale industrial datasets can be a challenging task due to the high number of users and items which are typically represented through ID embeddings. Such embeddings typically require a large amount of memory resour...