Generalized User Representations for Transfer Learning

arXiv:2403.00584v1 Announce Type: cross
Abstract: We present a novel framework for user representation in large-scale recommender systems, aiming at effectively representing diverse user taste in a generalized manner. Our approach employs a two-stage methodology combining representation learning and transfer learning. The representation learning model uses an autoencoder that compresses various user features into a representation space. In the second stage, downstream task-specific models leverage user representations via transfer learning instead of curating user features individually. We further augment this methodology …

abstract arxiv autoencoder cs.ir cs.lg diverse features framework generalized methodology novel recommender systems representation representation learning scale stage systems transfer transfer learning type