Elevating Group Recommendations and Collective Decisions Through Prioritized User Activities in Groups

Group modeling encompasses various areas of interest, including recommendations, movie watching, exercise performance, and the formation of social media groups with similar interests. Similarly, the GRS has numerous practical applications, such as books, movies, and television program recommendations. Various collaborative techniques, such as Least Misery, Average Voting, and Most Pleasure, to name a few, have been employed to enhance group recommendations. However, these methods are not without limitations,often introducing biases and yielding irrelevant suggestions. For example, group of people watching television, the active user having a remote control is paramount. Active user(s), who engage in activities like channel switching, rating, expressing preferences, and commenting, should hold significant influence. This study proposed and integrates active user engagement and feedback into the recommendation process, by considering user activities as feedback. The proposed system employs a filtering mechanismthat emphasizes the user’s activities, facilitating the prediction of relevant suggestions to group users. The experiments utilized the well-established benchmark dataset Movie Lens. The effectiveness of the proposed approach is evaluated using standard metrics such as precision, recall, and F-score. The results show that recommending active items to actively engaged user(s) significantly benefits most of the group users, yielding an improved suggestion. This study may help practitioners to build more robust recommender systems for groups.