The growing volume of digital data stimulates the adoption of recommender systems in di erent socioeconomic domains, including ecommerce, music, and news industries. While news recommenders help consumers deal with information overload and increase their engagement and satisfaction, their use also raises an increasing number of societal concerns, such as "Matthew e ects", " lter bubbles", and the overall lack of transparency. We argue that focusing on transparency for content-providers is an under-explored avenue. As such, we designed a simulation framework called SIREN 1 (SImulating Recommender E ects in online News environments), that allows news content providers to (i) select and parameterize di erent recommenders and (ii) analyze and visualize their e ects with respect to two diversity metrics with little costs. Taking the U.S. news media as a case study, we present an analysis on the recommender e ects with respect to long-tail novelty and unexpectedness using SIREN. Our analysis o ers a number of interesting ndings, such as the similar potential of certain algorithmically simple (item-based k-Nearest Neighbour) and sophisticated strategies (based on Bayesian Personalized Ranking) to increase diversity over time. Overall, we argue that simulating the e ects of recommender systems can help content providers to make more informed decisions when choosing algorithmic recommenders, and as such can help mitigate the aforementioned societal concerns.
Bountouridis et al. REF designed a simulation framework to see the effect of the recommendation models on the diversity and novelty of the Recommendations.
SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments
FAT* '19
Computer Science