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Sub-Matrix Factorization for Real-Time Vote Prediction

Published: 20 August 2020 Publication History

Abstract

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 (www.predikon.ch) 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.

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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, US 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 (www.predikon.ch) to provide real-time vote predictions in Switzerland and a data visualization tool to explore voting behavior.

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  • (2024)Analyzing Key Influencing Factors of University Teachers45 Use of Generative Artificial Intelligence in a Small-Sample Data EnvironmentProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675461(271-279)Online publication date: 19-Jan-2024
  • (2024)G-TransRec: A Transformer-Based Next-Item Recommendation With Time PredictionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335431511:3(4175-4188)Online publication date: Jun-2024
  • (2023)Multiview Deep Matrix Factorization for Shared Compact RepresentationIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317336710:5(2739-2751)Online publication date: Oct-2023

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cover image ACM Conferences
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
August 2020
3664 pages
ISBN:9781450379984
DOI:10.1145/3394486
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Published: 20 August 2020

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Author Tags

  1. data-driven political science
  2. generalized linear models
  3. matrix factorization
  4. vote prediction

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View all
  • (2024)Analyzing Key Influencing Factors of University Teachers45 Use of Generative Artificial Intelligence in a Small-Sample Data EnvironmentProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675461(271-279)Online publication date: 19-Jan-2024
  • (2024)G-TransRec: A Transformer-Based Next-Item Recommendation With Time PredictionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335431511:3(4175-4188)Online publication date: Jun-2024
  • (2023)Multiview Deep Matrix Factorization for Shared Compact RepresentationIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317336710:5(2739-2751)Online publication date: Oct-2023

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