Personalized recommendations are popular in these days of Internet driven activities, specifically shopping. Recommendation methods can be grouped into three major categories, content based filtering, collaborative filtering and machine learning enhanced. Information about products and preferences of different users are primarily used to infer preferences for a specific user. Inadequate information can obviously cause these methods to fail or perform poorly. The more information we provide to these methods, the more likely it is that the methods perform better. Knowledge graphs represent the current trend in recording information in the form of relations between entities, and can provide additional (side) information about products and users. Such information can be used to improve nearest neighbour search, clustering users and products, or train the neural network, when one is used. In this work, we present a new generic recommendation systems framework, that integrates knowledge graphs into the recommendation pipeline. We describe its software design and implementation, and then show through experiments, how such a framework can be specialized for a domain, say movie recommendations, and the improvements in recommendation results possible due to side information obtained from knowledge graphs representation of such information. Our framework supports different knowledge graph representation formats, and facilitates format conversion, merging and information extraction needed for training recommendation methods.
翻译:个人化的建议在互联网驱动活动、特别是购物的这些天里很受欢迎。建议方法可以分为三大类:内容过滤、合作过滤和机器学习。关于不同用户的产品和偏好的信息主要用来推断对特定用户的偏好。信息不足显然会导致这些方法失灵或不良。我们提供给这些方法的信息越多,方法就越有可能发挥更好的效果。知识图表代表了以实体关系形式记录信息的现有趋势,可以提供关于产品和用户的额外(侧面)信息。这类信息可用于改进近邻的搜索、集群用户和产品,或者在使用这种信息时培训神经网络。我们在此工作中提出了一个新的通用建议系统框架,将知识图表纳入建议管道。我们描述其软件的设计和实施,然后通过实验表明这种框架如何能专门用于某一领域,例如电影建议,以及建议中可能由于从这种信息的知识图表中获取的侧面信息而带来的改进结果。我们的框架支持不同的知识图表格式,并且便利为培训方法所需的格式转换、合并和信息提取。