Sub-Matrix Factorization for Real-Time Vote Prediction

We address the problem of predicting aggregate vote outcomes (e.g., national) from partial outcomes (e.g., regional) that are revealed sequentially. We combine matrix factorization techniques and generalized linear models (GLMs) to obtain a flexible, efficient, and accurate algorithm. This algorithm works in two stages: First, it learns representations of the regions from high-dimensional historical data. Second, it uses these representations to fit a GLM to the partially observed results and to predict unobserved results. We show experimentally that our algorithm is able to accurately predict the outcomes of Swiss referenda, U.S. presidential elections, and German legislative elections. We also explore the regional representations in terms of ideological and cultural patterns. Finally, we deploy an online Web platform ( to provide real- time vote predictions in Switzerland and a data visualization tool to explore voting behavior. A by-product is a dataset of sequential vote results for 330 referenda and 2196 Swiss municipalities.

Published in:
[KDD '20: Proceedings of the 26th ACM SIGKDD international conference on Knowledge discovery and data mining]
Presented at:
The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - KDD ’20, Virtual Event,, August 23–27, 2020
Aug 23 2020
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 Record created 2020-07-25, last modified 2020-07-27

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