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.

翻译：推荐系统研究通常聚焦于基于包含数百万用户交互的大规模数据集的方法。然而，许多小企业由于数据可用性极为有限，难以应用最先进的模型。我们提出一种基于图的推荐模型，该模型利用用户与不同类型内容之间的异构交互，能够在小型数据集上良好运行。通过遗传算法寻找表征用户与内容之间关系强度的最优权重。在面向研究社区公开的两个真实世界数据集上的实验表明，与低数据环境下的其他先进方法相比，本方法取得了显著成效（提升高达7%）。这些改进具有统计显著性，并在不同数据样本中表现一致。