Why am I reading this? Explaining Personalized News Recommender Systems

Abstract

Social media and online platforms significantly impact what millions of people get exposed to daily, mainly through recommended content. Hence, recommendation processes have to benefit individuals and society. With this in mind, we present the visual workspace NewsRecXplain, with the goals of (1) explaining and raising awareness about recommender systems, (2) enabling individuals to control and customize news recommendations, and (3) empowering users to contextualize their news recommendations to escape from their filter bubbles. This visual workspace achieves these goals by allowing users to configure their own individualized recommender system, whose news recommendations can then be explained within the workspace by way of embeddings and statistics on content diversity.