  Group recommender systems (GRS) are critical in discovering relevant items
from a near-infinite inventory based on group preferences rather than
individual preferences, like recommending a movie, restaurant, or tourist
destination to a group of individuals. The traditional models of group
recommendation are designed to act like a black box with a strict focus on
improving recommendation accuracy, and most often, they place the onus on the
users to interpret recommendations. In recent years, the focus of Recommender
Systems (RS) research has shifted away from merely improving recommendation
accuracy towards value additions such as confidence and explanation. In this
work, we propose a conformal prediction framework that provides a measure of
confidence with prediction in conjunction with a group recommender system to
augment the system-generated plain recommendations. In the context of group
recommender systems, we propose various nonconformity measures that play a
vital role in the efficiency of the conformal framework. We also show that
defined nonconformity satisfies the exchangeability property. Experimental
results demonstrate the effectiveness of the proposed approach over several
benchmark datasets. Furthermore, our proposed approach also satisfies validity
and efficiency properties.
