Wordle the Puzzle: A Prediction and Classification Model based on Com-prehensive RNN
Authors: 
Zixu Shen, Yong Pan, Anbo WangAuthors Info & Claims
Zixu Shen, Yong Pan, Anbo WangAuthors Info & ClaimsPages 305 - 309
Abstract
This article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. To further enhance the gaming experience, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the distribution of number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the ARIMIA model was proposed. To address the problem of predicting the distribution of number of attempts, the Gated Recurrent Neural Network model was proposed. In order to effectively improve the user experience, we have further mined the data. In regards to the problem of classifying word difficulty, the AHP-Kmeans clustering model was proposed. Finally, this paper contributes to the difficulty classification of words based on different word attributes.
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- Wordle the Puzzle: A Prediction and Classification Model based on Com-prehensive RNN
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Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 April 2024
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ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
Hangzhou, China
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