Element-Wise Alternating Least Squares Algorithm for Nonnegative Matrix Factorization on One-Hot Encoded Data
| العنوان: | Element-Wise Alternating Least Squares Algorithm for Nonnegative Matrix Factorization on One-Hot Encoded Data |
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| المؤلفون: | Zhuo Wu, Tsuyoshi Migita, Norikazu Takahashi |
| المصدر: | Communications in Computer and Information Science ISBN: 9783030638221 ICONIP (5) |
| بيانات النشر: | Springer International Publishing, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | 021103 operations research, Computer science, Feature vector, 0211 other engineering and technologies, 02 engineering and technology, Recommender system, Matrix decomposition, Non-negative matrix factorization, Matrix (mathematics), Convergence (routing), 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, 020201 artificial intelligence & image processing, Categorical variable, Algorithm |
| الوصف: | Matrix factorization is a popular technique used in recommender systems based on collaborative filtering. Given a matrix that represents ratings of items by users, one can obtain latent feature vectors of the users and the items by applying one of the existing matrix factorization algorithms. In this paper, we focus our attention on matrices obtained from categorical ratings using one-hot encoding, and propose an element-wise alternating least squares algorithm to obtain latent feature vectors from such matrices. We next show that the proposed algorithm has the global convergence property in the sense of Zangwill. We also show through experiments using a benchmark dataset that the proposed algorithm is effective for prediction of unknown ratings. |
| ردمك: | 978-3-030-63822-1 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::775383a652d44baa1c2ad0298d5a0a5d https://doi.org/10.1007/978-3-030-63823-8_40 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........775383a652d44baa1c2ad0298d5a0a5d |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783030638221 |
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