Igor Brigadir

Reshma Narayanan Kutty, Claudia Orellana-Rodriguez, Igor Brigadir et al.

News recommendation and personalization is not a solved problem. People are growing concerned of their data being collected in excess in the name of personalization and the usage of it for purposes other than the ones they would think reasonable. Our experience in building personalization products for publishers while adhering to safeguard user privacy led us to investigate more on the user perspective of privacy and personalization. We conducted a survey to explore people's experience with personalization and privacy and the viewpoints of different age groups. In this paper, we share our major findings with publishers and the community that can inform algorithmic design and implementation of the next generation of news recommender systems, which must put the human at its core and reach a balance between personalization experiences and privacy to reap the benefits of both.

Ernesto Diaz-Aviles, Claudia Orellana-Rodriguez, Igor Brigadir et al.

Newsletters have (re-) emerged as a powerful tool for publishers to engage with their readers directly and more effectively. Despite the diversity in their audiences, publishers' newsletters remain largely a one-size-fits-all offering, which is suboptimal. In this paper, we present NU:BRIEF, a web application for publishers that enables them to personalize their newsletters without harvesting personal data. Personalized newsletters build a habit and become a great conversion tool for publishers, providing an alternative readers-generated revenue model to a declining ad/clickbait-centered business model.

Igor Brigadir, Derek Greene, Pádraig Cunningham

Twitter is often the most up-to-date source for finding and tracking breaking news stories. Therefore, there is considerable interest in developing filters for tweet streams in order to track and summarize stories. This is a non-trivial text analytics task as tweets are short, and standard retrieval methods often fail as stories evolve over time. In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories. We evaluate the effectiveness of these mechanisms on a number of recent news events for which manually curated timelines are available. Assessments based on ROUGE metrics indicate that an adaptive approaches are best suited for tracking evolving stories on Twitter.