A Consensus-Focused Group Recommender System
This addresses group recommendation for social event planning, but it is incremental as it builds on existing group decision-making principles.
The paper tackles the problem of recommending social events to groups by facilitating consensus decision-making, and the proposed decision cascading model achieves better prediction performance than independent decision-making models.
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.