Enhancing group recommendation performance by integrating individual prediction uncertainty

The uncertainty in the prediction of a single rating by recommender systems can vary significantly owing to the diverse historical interaction data between users and items under a given recommendation model. In group recommendations, high uncertainty in individual rating predictions may lead to erroneous group decisions. However, previous studies have often overlooked the impact of the uncertainty of individual rating predictions in the group recommendation process. To address this, this study proposes a measurement method for individual prediction certainty that employs the validity of bipartite graph voting. In addition, a group recommendation algorithm named consideration member reliability group recommendation (CMRGR), which integrates the individual prediction uncertainty of each group member, is presented. The results of experiments on the MovieLens-1M, Netflix, and MovieTweetings datasets show that the CMRGR algorithm improved the recommendation accuracy by at least 10% compared with the baseline. Moreover, incorporating the prediction uncertainty into recommendations was found to have approximately twice the impact on group recommendation accuracy compared with individual recommendation accuracy.