Enhanced Group Recommendations using member inclination and item usefulness

The purpose of group recommender systems (GRS) is to recommend relevant items to group members rather than individuals. The suggestion relies entirely on obtaining the preferences of groups, which is mostly dependent on trust, tendency, influence, likeness, closeness, etc among the group of users. One of the common and current issues in GRS is member relationships and group satisfaction. To improve the group recommendation and group satisfaction, we introduce member inclination and item usefulness in a group. The proposed method is discussed in two ways by using the aggregate prediction approach of GRS. In the first case, we compute user inclination and item usefulness considering the whole dataset. In the second case, we compute member inclination and item usefulness using only group information. We introduce a novel aggregation strategy based on popularity and likeness (PLAS). It combines the predicted member rating into a group score.To evaluate the group satisfaction, we propose a satisfaction measure for the group (SMG). Experiment is conducted on MovieLense-1M (ML-1M), MovieLense-100k (ML-100k), and NetFlix-1M (NF-1M) datasets. The proposed novel approach outperforms the existing state-of-art method in various measures like MAE, RMSE, Precision, Recall, F-1 measure, GIM, GPIM, and SMG.