Dealing with Item Cold-Start in News Recommender at Globo

Globo is the largest Latin American mass media group, where its vertical information portals play an important role in content distribution. Among such portals, G1 is Globo’s journalism portal, being the most popular news portal in Brazil and responsible for delivering informative content to more than 100 million unique users per day. In this context, recommender systems play an important role in achieving a good user experience, offering personalized content. In this paper, we discuss how G1’s recommender system identifies and deals with the item cold-start problem, describing the recommendation scenarios and how the applied improvements in the currently deployed algorithms led to a decreased processing time and an increased CTR in the context of news recommendations.