Improving Group Recommendation using Revised Slope One

The predisposition of researchers in recommender systems (RS) is increasing due to efficient and personalized recommendations. RS is not able to recommend a product to a group of people. Recently, there has been a lot of interest in group recommendations. Currently, several machine learning and deep learning-based techniques are used to understand the group recommender system (GRS) for goods and find the suitable next items for a group. The major issue of GRS is to satisfy individual member choices in a group. The task of GRS has been accomplished in group formation, individual member rating prediction, and aggregation. This study proposed a revised Slope One predictor in GRS to address this issue. We use the revised Slope One predictor to predict individual member ratings. Preliminary outcomes using two real-world datasets are quite encouraging. The proposed method performs well as compared to the original Slope One.