Ernesto Diaz-Aviles, recsyslabs and University College Dublin (UCD), Ireland, [email protected]
Claudia Orellana-Rodriguez, recsyslabs and University College Dublin (UCD), Ireland, [email protected]
Igor Brigadir, recsyslabs and University College Dublin (UCD), Ireland, [email protected]
Reshma Narayanan Kutty, recsyslabs and University College Dublin (UCD), Ireland, [email protected]
RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, Netherlands, September 2021
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.
CCS Concepts: • Information systems → Recommender systems; • Computing methodologies → Machine learning;
Keywords: Newsletter Personalization, AI, Federated Learning, ML, NLP, Privacy, Personalized Ranking
ACM Reference Format: Ernesto Diaz-Aviles, Claudia Orellana-Rodriguez, Igor Brigadir, and Reshma Narayanan Kutty. 2021. NU:BRIEF – A Privacy-aware Newsletter Personalization Engine for Publishers. In Fifteenth ACM Conference on Recommender Systems (RecSys '21), September 27-October 1, 2021, Amsterdam, Netherlands. ACM, New York, NY, USA
PDF: https://github.com/recsyslabs/2021-recsys-paper/blob/main/recsys21-nubrief-by-recsyslabs.pdf