  Recommender systems, crucial for user engagement on platforms like e-commerce
and streaming services, often lag behind users' evolving preferences due to
static data reliance. After Temporal Graph Networks (TGNs) were proposed,
various studies have shown that TGN can significantly improve situations where
the features of nodes and edges dynamically change over time. However, despite
its promising capabilities, it has not been directly applied in recommender
systems to date. Our study bridges this gap by directly implementing Temporal
Graph Networks (TGN) in recommender systems, a first in this field. Using
real-world datasets and a range of graph and history embedding methods, we show
TGN's adaptability, confirming its effectiveness in dynamic recommendation
scenarios.
