Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to enrich both item and user representations. Motivated by the use of Transformers for understanding rich text in content-based filtering recommender systems, we propose Content-aware KG-enhanced Meta-preference Networks as a way to enhance collaborative filtering recommendation based on both structured information from KG as well as unstructured content features based on Transformer-empowered content-based filtering. To achieve this, we employ a novel training scheme, Cross-System Contrastive Learning, to address the inconsistency of the two very different systems and propose a powerful collaborative filtering model and a variant of the well-known NRMS system within this modeling framework. We also contribute to public domain resources through the creation of a large-scale movie-knowledge-graph dataset and an extension of the already public Amazon-Book dataset through incorporation of text descriptions crawled from external sources. We present experimental results showing that enhancing collaborative filtering with Transformer-based features derived from content-based filtering outperforms strong baseline systems, improving the ability of knowledge-graph-based collaborative filtering systems to exploit item content information.
翻译:以知识图(KG)为基础的合作过滤法(Cooperation Filter)是一种有效的方法,通过利用KG的结构化信息来丰富项目和用户表达方式,使相对静态的领域(如电影和书籍)的建议系统个性化化,利用KG的结构化信息丰富,丰富项目和用户表达方式。由于使用变换器来理解内容过滤建议系统中内容丰富的内容,我们提议采用内容觉悟KG-enhanced Meta-ference Network 网络,以此加强合作过滤建议,既基于KG的结构化信息,又基于基于变压器-以内容为基础的过滤器内容过滤器的无结构化内容功能。为此,我们采用了新型培训计划,即交叉系统对比学习,以解决两个非常不同的系统的不一致之处,并在此模型框架内提议一个强大的协作过滤模型和知名的NRMS系统变体。我们还通过创建大型的电影-知识绘图数据集和扩展已经公开的亚马逊-Book数据集,通过吸收外部来源的文本描述来扩展。我们提出实验结果,显示,通过基于变换系统的能力过滤系统改进了基于内容的强大基础的系统的基本数据系统,从而改进了以改进了基于内容过滤系统。