On the Predictability of Talk Attendance at Academic Conferences

TL;DR

This study investigates the predictability of talk attendance at academic conferences by analyzing face-to-face contact data and user interests, applying a novel unsupervised machine learning approach to assess their predictive power.

Contribution

It introduces the use of Hybrid Rooted PageRank to combine contact and interest networks for predicting conference talk attendance, providing insights into their relative effectiveness.

Findings

Contact and interest networks have similar predictive power.

Combining networks offers limited improvements in prediction accuracy.

Face-to-face contact data can effectively predict conference attendance.

Abstract

This paper focuses on the prediction of real-world talk attendances at academic conferences with respect to different influence factors. We study the predictability of talk attendances using real-world tracked face-to-face contacts. Furthermore, we investigate and discuss the predictive power of user interests extracted from the users' previous publications. We apply Hybrid Rooted PageRank, a state-of-the-art unsupervised machine learning method that combines information from different sources. Using this method, we analyze and discuss the predictive power of contact and interest networks separately and in combination. We find that contact and similarity networks achieve comparable results, and that combinations of different networks can only to a limited extend help to improve the prediction quality. For our experiments, we analyze the predictability of talk attendance at the ACM Conference on Hypertext and Hypermedia 2011 collected using the conference management system Conferator.