Dealing with Item Cold-Start in News Recommender at Globo

Record type:

paper

Creator:

JoeLetFel

Publisher:

Soc

Host:

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.

Mining news articles dealing with food security

International audience Food security is a major concern in West Africa, particularly

aijournalist/AI-Powered-News-Recommender-System

In an era of information overload, I developed an AI-driven news recommender system to help readers

Dealing with Elusive Futures

The time to come - as well as the exploration thereof - remains elusive for social actors and social

ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark

Recommender-system research for Vietnamese remains limited by the absence of a public, well-document

Dealing with Estranged Political Relationships

This data is related to the role of the SPLM in the conflict in South Sudan. Data collection involve

Evolution of cold gas at 2

We present results from a spectroscopically blind search for associated and intervening HI 21-cm and

Dealing with Item Cold-Start in News Recommender at Globo

Record type:

Visit

Tasks