  Content-based news recommendation systems need to recommend news articles
based on the topics and content of articles without using user specific
information. Many news articles describe the occurrence of specific events and
named entities including people, places or objects. In this paper, we propose a
graph traversal algorithm as well as a novel weighting scheme for cold-start
content based news recommendation utilizing these named entities. Seeking to
create a higher degree of user-specific relevance, our algorithm computes the
shortest distance between named entities, across news articles, over a large
knowledge graph. Moreover, we have created a new human annotated data set for
evaluating content based news recommendation systems. Experimental results show
our method is suitable to tackle the hard cold-start problem and it produces
stronger Pearson correlation to human similarity scores than other cold-start
methods. Our method is also complementary and a combination with the
conventional cold-start recommendation methods may yield significant
performance gains. The dataset, CNRec, is available at:
https://github.com/kevinj22/CNRec
