  Multi-domain recommender systems benefit from cross-domain representation
learning and positive knowledge transfer. Both can be achieved by introducing a
specific modeling of input data (i.e. disjoint history) or trying dedicated
training regimes. At the same time, treating domains as separate input sources
becomes a limitation as it does not capture the interplay that naturally exists
between domains. In this work, we efficiently learn multi-domain representation
of sequential users' interactions using graph neural networks. We use temporal
intra- and inter-domain interactions as contextual information for our method
called MAGRec (short for Multi-domAin Graph-based Recommender). To better
capture all relations in a multi-domain setting, we learn two graph-based
sequential representations simultaneously: domain-guided for recent user
interest, and general for long-term interest. This approach helps to mitigate
the negative knowledge transfer problem from multiple domains and improve
overall representation. We perform experiments on publicly available datasets
in different scenarios where MAGRec consistently outperforms state-of-the-art
methods. Furthermore, we provide an ablation study and discuss further
extensions of our method.
