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:建议系统研究往往侧重于使用包含数百万用户互动的大型数据集的方法,然而,许多小企业由于数据供应非常有限而难以采用最先进的模型。我们提出了一个基于图表的建议模型,利用不同类型用户和内容之间的不同互动,能够在小规模数据集上很好地运作。利用基因算法找到代表用户和内容之间关系的力量的最佳权重。两个真实世界数据集(我们提供给研究界)的实验(我们提供给研究界)显示,与低数据环境的其他最新方法相比,这些改进在统计上意义重大,而且在不同的数据样本中是一致的。