The rich-club phenomenon describes the tendency of well-connected nodes to form highly cohesive cores within networks, offering insights into their fundamental structure. This paper presents the Rich-Club Integrated (RCI) model, developed for predicting missing edges in large and complex bipartite networks, which is particularly relevant for product recommendation systems. By incorporating the rich-club coefficient, RCI demonstrates enhanced performance compared to models that do not account for this phenomenon. This study highlights the practical utility of the rich-club coefficient in enhancing recommendation systems and encourages further exploration of this approach in network analysis.