  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.
