To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB). In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase. Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e. popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset.
翻译:为了开发一个具有知识意识的推荐系统,一个关键的数据问题是如何为推荐系统项目获取丰富和结构化的知识信息。现有的数据集或方法要么使用原始推荐系统(含有极少种类的有用信息)的侧面信息,要么使用私人知识库(KB)。在本文中,我们介绍了推荐系统的第一个公共链接的KB数据集,名为KB4Rec v1.0,它将三个广泛使用的RS数据集与流行的KB Freebase联系起来。根据我们链接的数据集,我们首先预设了一些有趣的定性分析实验,其中我们讨论了两个重要因素(即受欢迎度和耐久性)对是否可将一个RS项目与KB实体联系起来的影响。最后,我们比较了我们链接的数据集上的若干有知识意识的建议算法。