Kishor Thapa

Papers

Total Citations

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About

Kishor Thapa is a researcher whose work sits at the intersection of machine learning, network science, and multimedia analytics. His primary research focuses on developing advanced graph-based and deep learning models to understand and predict user behavior in digital environments. Thapa’s most notable contribution is his work on "Predicting video engagement using heterogeneous DeepWalk," which introduced a novel application of heterogeneous network embeddings to forecast how audiences interact with online video content. This paper, published in 2021, has garnered 23 citations, reflecting its relevance in the rapidly evolving field of recommendation systems and content analytics. By leveraging the structural properties of user-content interaction graphs, Thapa’s approach offers a more nuanced understanding of engagement beyond simple view counts. His research has practical implications for platforms seeking to optimize content delivery and user retention. Thapa’s work stands out for bridging theoretical graph embedding techniques with real-world, large-scale prediction tasks, making his contributions valuable for both academic researchers and industry practitioners working on the frontier of personalized media experiences.