  Recommender system research has oftentimes focused on approaches that operate
on large-scale datasets containing millions of user interactions. However, many
small businesses struggle to apply state-of-the-art models due to their very
limited availability of data. We propose a graph-based recommender model which
utilizes heterogeneous interactions between users and content of different
types and is able to operate well on small-scale datasets. A genetic algorithm
is used to find optimal weights that represent the strength of the relationship
between users and content. Experiments on two real-world datasets (which we
make available to the research community) show promising results (up to 7%
improvement), in comparison with other state-of-the-art methods for low-data
environments. These improvements are statistically significant and consistent
across different data samples.
