ABSTRACT: The increasing digitization of our society radically changes how we use digital media, exchange information, and make decisions. This development also changes how social scientists collect data on human behavior and experience in the field. One new form of data comes from in-vivo high-frequency mobile sensing via smartphones. Mobile sensing allows for the investigation of formerly intangible psychological constructs with objective data. In particular mobile sensing enables fine-grained, longitudinal data collections in the wild and at large scale. The additional combination of mobile sensing with state of the art machine learning methods, provides a perspective for the direct prediction of psychological traits and behavioral outcomes from these data. In this talk I will give an overview on my work combining machine learning with mobile sensing and discuss the opportunities and limitations of this approach. Consequently, I will provide an outlook perspective on where the routine use of mobile psychological sensing could take research and society alike.
Please join the Center for Data Science (CEDAS, UiB) and MediaFutures for an invited talk by Himan Abdollahpouri from the Northwestern University, USA, about the topic of popularity bias in recommender systems. Welcome to all! TITLE: User-centered Investigation of Popularity Bias in Recommender Systems. WHEN: 6 May 2021, 14:15-15:00 WHERE: https://uib.zoom.us/j/63657771765?pwd=anZlNkVPdkxoQ0FmZit5WDJ0R3FkQT09 ABSTRACT: Recommendation and ranking […]