Social media has become a tremendous source to bring in new clients. Sharing posts for new offers/products to get extensive client engagement can be predicted by grouping the users based on their previous interactions. In this paper, we improve existing state-of-the-art techniques to predict group-level popularity by extending the data clustering approach and constraint network prediction using stochastic Adam optimization. Various other topological properties of this two-level approach are also tested. The Adam optimization for clustered group prediction improves the relative error substantially. Overall, the proposed novel approach improved the prediction popularity accuracy by a significant difference of 18.21%.

社交媒体已成为吸引新客户的巨大来源。通过根据用户以前的互动将用户分组，可以预测分享新优惠/产品的帖子以吸引广泛的客户参与。在本文中，我们通过扩展数据聚类方法和使用随机Adam优化的约束网络预测来改进现有的先进技术，以预测组级别的受欢迎程度。还测试了此两级方法的各种其他拓扑属性。聚类预测的亚当优化极大地改善了相对误差。总体而言，所提出的新颖方法将预测受欢迎程度的准确性提高了18.21％。