Recommender Systems for Local Journalism
In-Person Event
Northwestern University Evanston Campus
McCormick School of Engineering
Mudd Hall, Room 3514
2233 Tech Dr, Evanston, IL 60208
For directions and parking information, refer to the campus map
Recommender Systems for Local Journalism
This talk traces the evolution of recommender systems from early heuristics and matrix factorization to modern learning-to-rank and LLM-enhanced approaches, then looks ahead to where the field is going. After a brief cross-domain survey, the focus narrows to news and local-news recommenders.
Payam will present his recent research along with a forward-looking vision for how tailored recommender systems can help revitalize local journalism and support more informed, engaged communities.
Payam Pourashraf works on the Advanced Capabilities (R&D) team at a financial company in Illinois. With a background spanning software, hardware, and computer science, he brings a full-stack perspective to his work. His recent academic research focuses on recommender systems for news, with a particular emphasis on supporting local journalism.
Beyond his industry role, Payam is committed to growing the region’s technical community as the elected Vice-Chair of ACM Chicago. He is also passionate about education, having spent five years as an adjunct faculty member at DePaul University, where he taught more than 30 course offerings in Python and applied data science.