Abstract:

Group recommendations, where a system suggests items for a group of users rather than individuals, present a unique challenge in collaborative filtering. Traditional approaches to group recommendations often rely on aggregating individual preferences, which can lead to suboptimal results when preferences are diverse or conflicting. This paper explores a novel approach using Neural Collaborative Filtering (NCF) to improve group recommendation accuracy. NCF, which leverages deep learning techniques to model complex user-item interactions, offers a more nuanced understanding of group dynamics by incorporating both individual and group-level preferences. We propose a new NCF-based framework designed to handle group recommendations by effectively learning and integrating diverse user preferences. Our experimental results demonstrate that the proposed approach outperforms traditional group recommendation methods in terms of prediction accuracy and user satisfaction. This research highlights the potential of neural networks in enhancing group recommendation systems and provides a foundation for future developments in this area.

Description:

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