  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.
