Group level social media popularity prediction by MRGB and Adam optimization

Navdeep Bohra () and Vishal Bhatnagar
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Navdeep Bohra: GGSIPU
Vishal Bhatnagar: NSUT (Formally Ambedkar Institute Of Advanced Communication Technologies And Research)

Journal of Combinatorial Optimization, 2021, vol. 41, issue 2, No 5, 328-347

Abstract: Abstract 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%.

Keywords: Content prediction; Tensor decomposition; Graph clustering; Adam optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10878-020-00684-z

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