  Inductive transfer learning has had a big impact on computer vision and NLP
domains but has not been used in the area of recommender systems. Even though
there has been a large body of research on generating recommendations based on
modeling user-item interaction sequences, few of them attempt to represent and
transfer these models for serving downstream tasks where only limited data
exists.
  In this paper, we delve on the task of effectively learning a single user
representation that can be applied to a diversity of tasks, from cross-domain
recommendations to user profile predictions. Fine-tuning a large pre-trained
network and adapting it to downstream tasks is an effective way to solve such
tasks. However, fine-tuning is parameter inefficient considering that an entire
model needs to be re-trained for every new task. To overcome this issue, we
develop a parameter efficient transfer learning architecture, termed as
PeterRec, which can be configured on-the-fly to various downstream tasks.
Specifically, PeterRec allows the pre-trained parameters to remain unaltered
during fine-tuning by injecting a series of re-learned neural networks, which
are small but as expressive as learning the entire network. We perform
extensive experimental ablation to show the effectiveness of the learned user
representation in five downstream tasks. Moreover, we show that PeterRec
performs efficient transfer learning in multiple domains, where it achieves
comparable or sometimes better performance relative to fine-tuning the entire
model parameters. Codes and datasets are available at
https://github.com/fajieyuan/sigir2020_peterrec.
