LiMAML: Personalization of Deep Recommender Models via Meta Learning

arXiv:2403.00803v1 Announce Type: cross
Abstract: In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members …

abstract adoption arxiv business cs.ai cs.ir cs.lg delivery diverse expand meta modeling networks neural networks paradigm personalization recommender systems significance systems type updates via