Summary of Fast and Adaptive Questionnaires For Voting Advice Applications, by Fynn Bachmann et al.
Fast and Adaptive Questionnaires for Voting Advice Applications
by Fynn Bachmann, Cristina Sarasua, Abraham Bernstein
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC); Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel adaptive questionnaire approach is introduced to enhance the effectiveness of Voting Advice Applications (VAAs) by reducing user fatigue and incomplete responses. This method selects subsequent questions based on users’ previous answers, leveraging an encoder and decoder module that predicts missing values at any completion stage. A selector module determines the most informative subsequent question based on the voter’s current position in a two-dimensional latent space and the remaining unanswered questions. The approach is validated using the Smartvote dataset from the Swiss Federal elections in 2019, achieving 74% accuracy after asking the same number of questions as in the condensed version. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Voting Advice Applications can be improved by making them easier to use. One way to do this is to ask fewer questions while still getting accurate results. This paper presents a new way to do just that. It uses a special kind of computer program to predict what people will say, based on their answers so far. The program helps the application decide which questions to ask next, and when it should stop asking questions altogether. The approach was tested using real data from a recent election in Switzerland, and it worked well. |
Keywords
* Artificial intelligence * Decoder * Encoder * Latent space
