IBGR: Influence-Based Group Recommendation system
Reza Barzegar Nozari, Hamidreza Koohi, Ziad Kobti
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Abstract
In the era of personalized digital experiences, recommendation systems are crucial. Influence-Based Group Recommender (IBGR) addresses the challenge of group decision-making. It combines fuzzy clustering, leader identification, trust metrics, and influence calculations to offer comprehensive group recommendations. Experiments show IBGR outperforms alternatives, emphasizing the importance of leader influence and group compatibility. Accessible via Python repositories, IBGR invites research and practical use. Despite its potential, IBGR requires real-world testing and interface improvements. Nonetheless, it marks a significant advancement in group recommendations, promising enhanced group experiences in the digital age.