  In many cases, recommendations are consumed by groups of users rather than
individuals. In this paper, we present a system which recommends social events
to groups. The system helps groups to organize a joint activity and
collectively select which activity to perform among several possible options.
We also facilitate the consensus making, following the principle of group
consensus decision making. Our system allows users to asynchronously vote, add
and comment on alternatives. We observe social influence within groups through
post-recommendation feedback during the group decision making process. We
propose a decision cascading model and estimate such social influence, which
can be used to improve the performance of group recommendation. We conduct
experiments to measure the prediction performance of our model. The result
shows that the model achieves better results than that of independent decision
making model.
