Ranking academic institutions on potential paper acceptance in upcoming conferences

TL;DR

This paper presents a method to rank research institutions based on predicted paper acceptance at upcoming conferences using exponential smoothing models, addressing a challenge from KDD Cup 2016.

Contribution

It introduces a two-step approach combining paper identification and exponential smoothing for predicting conference acceptance, improving institutional ranking accuracy.

Findings

Achieved an overall score of 0.7508 in KDD Cup 2016

Used exponential smoothing models for prediction

Outperformed baseline methods

Abstract

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.