  In recent years, the integration of federated learning (FL) and
recommendation systems (RS), known as Federated Recommendation Systems (FRS),
has attracted attention for preserving user privacy by keeping private data on
client devices. However, FRS faces inherent limitations such as data
heterogeneity and scarcity, due to the privacy requirements of FL and the
typical data sparsity issues of RSs. Models like ChatGPT are empowered by the
concept of transfer learning and self-supervised learning, so they can be
easily applied to the downstream tasks after fine-tuning or prompting. These
models, so-called Foundation Models (FM), fouce on understanding the human's
intent and perform following their designed roles in the specific tasks, which
are widely recognized for producing high-quality content in the image and
language domains. Thus, the achievements of FMs inspire the design of FRS and
suggest a promising research direction: integrating foundation models to
address the above limitations. In this study, we conduct a comprehensive review
of FRSs with FMs. Specifically, we: 1) summarise the common approaches of
current FRSs and FMs; 2) review the challenges posed by FRSs and FMs; 3)
discuss potential future research directions; and 4) introduce some common
benchmarks and evaluation metrics in the FRS field. We hope that this position
paper provides the necessary background and guidance to explore this
interesting and emerging topic.
