The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent recommendation methods based on graph embeddings have shown state-of-the-art performance. In general, these methods encode latent rating patterns and content features. Differently from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Additionally, our method has the advantage of providing explanations that involve the coverage of aspect-based opinions given by users about recommended items.

翻译：神经网络嵌入的成功意味着人们重新有兴趣使用知识图表进行各种机器学习和信息检索任务,特别是最近基于图表嵌入的建议方法显示了最新业绩。一般而言,这些方法编码了潜在的评级模式和内容特征。与以前的工作不同,我们在本文件中提议利用从图表中提取的嵌入,这些图中综合了从评分和文字审查中表达的基于方面的意见。然后,我们调整和评价了亚马逊和叶尔普审查产生的六个领域的图表的最新图表嵌入技术,优于基线建议者。此外,我们的方法优势在于提供解释,涉及用户对推荐项目提出的基于方面的意见。