  Item-to-Item (I2I) recommendation is an important function in most
recommendation systems, which generates replacement or complement suggestions
for a particular item based on its semantic similarities to other cataloged
items. Given that subsets of items in a recommendation system might be
co-interacted with by the same set of customers, graph-based models, such as
graph neural networks (GNNs), provide a natural framework to combine, ingest
and extract valuable insights from such high-order relational interactions
between cataloged items, as well as their metadata features, as has been shown
in many recent studies. However, learning GNNs effectively for I2I requires
ingesting a large amount of relational data, which might not always be
available, especially in new, emerging market segments. To mitigate this data
bottleneck, we postulate that recommendation patterns learned from existing
mature market segments (with private data) could be adapted to build effective
warm-start models for emerging ones. To achieve this, we propose and
investigate a personalized federated modeling framework based on GNNs to
summarize, assemble and adapt recommendation patterns across market segments
with heterogeneous customer behaviors into effective local models. Our key
contribution is a personalized graph adaptation model that bridges the gap
between recent literature on federated GNNs and (non-graph) personalized
federated learning, which either does not optimize for the adaptability of the
federated model or is restricted to local models with homogeneous
parameterization, excluding GNNs with heterogeneous local graphs.
