Understanding complex information behaviour in an empirical, ecologically valid way is difficult at the best of times. It verges on impossible with artificial environments and tightly constrained tasks such as those found in usability labs. Similarly artificial measures of user satisfaction that are readily available from test collections are unlikely to provoke revolutionary insights. The alternative is to study user behaviour in the wild, however this involves complex trade-offs between method, precision, privacy, interpretation and generalizability. This tutorial addresses both qualitative and quantitative methods, and how to balance the risks of studying unpredictable but fully human behaviour, particularly during and following a pandemic.
* Two lecturers will present different material in each session, and participants are welcome to attend both. The content of the second session is not, however, dependent on the first, and participants may attend only one session if they prefer, and either session is fine.
Research in conversational information seeking (CIS) is moving very rapidly in various directions such as user interaction, system design, and evaluation. The tutorial focuses on the theoretical foundations and information-seeking processes for CIS, as well as their evaluation. The tutorial aims to introduce and communicate CIS research to the community and discuss it from different perspectives, such as theoretical modelling, evaluation, and user simulation. Also, it aims at gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
* The tutorial will run in two independent sessions. These are complementary, but participation in both is not necessary.
Personalized recommender systems are essential tools to facilitate human decision making. Many contemporary recommender systems use advanced machine learning techniques to model and predict user preferences from behavioral data. While such systems can provide helpful recommendations, their algorithms’ design does not incorporate the underlying psychological mechanisms that shape user preferences and behavior. In this tutorial, we will guide the attendees through the state-of-the-art in psychology-informed recommender systems, i.e., recommender systems that consider extrinsic and intrinsic human factors. We show how such systems can improve the recommendation process in a user-centric fashion.
14 March, 15:00 – 18:00 CET