Recommender systems were originally developed as interactive
intelligent systems that can proactively guide users to items that
match their preferences. Despite its origin on the crossroads of HCI
and AI, the majority of research on recommender systems gradually
focused on objective accuracy criteria paying less and less attention
to how users interact with the system as well as the efficacy of
interface designs from users’ perspectives. This trend is reversing
with the increased volume of research that looks beyond algorithms,
into users’ interactions, decision making processes, and overall
experience. The series of workshops on Interfaces and Human
Decision Making for Recommender Systems focuses on the "human
side" of recommender systems. The goal of the research stream
featured at the workshop is to improve users’ overall experience
with recommender systems by integrating different theories of
human decision making into the construction of recommender
systems and exploring better interfaces for recommender systems.
In this summary,we introduce the JointWorkshop on Interfaces and
Human Decision Making for Recommender Systems at RecSys’21,
review its history, and discuss most important topics considered at
the workshop