Ranking academic institutions on potential paper acceptance in upcoming conferences
This addresses a specific data mining challenge for academic ranking, but it is incremental as it builds on existing methods for a competition task.
The paper tackled the problem of ranking academic institutions based on predicted paper acceptances in top-tier conferences, using a two-step approach with exponential smoothing models, achieving an overall score of 0.7508 compared to the winning score of 0.7656.
The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications. Rank importance of research institutions are derived from predictions on the number of full research papers that would potentially get accepted in upcoming top-tier conferences, utilizing public information on the web. This paper describes our solution to KDD Cup 2016. We used a two step approach in which we first identify full research papers corresponding to each conference of interest and then train two variants of exponential smoothing models to make predictions. Our solution achieves an overall score of 0.7508, while the winning submission scored 0.7656 in the overall results.