TY - GEN
T1 - Element-Wise Alternating Least Squares Algorithm for Nonnegative Matrix Factorization on One-Hot Encoded Data
AU - Wu, Zhuo
AU - Migita, Tsuyoshi
AU - Takahashi, Norikazu
N1 - Funding Information:
This research was partially supported by the STRADA (Stud-ies on TRaffic Accident Data Analysis) project. The authors would like to thank the core members of this project: Makoto Maeda and Tadashi Koriki of TOSCO Corporation, Takafumi Komoto and Chihiro Egi of Okayama Prefectural Police, and Takayuki Shuku of Okayama University, for their valuable comments on one-hot encoding and nonnegative matrix factorization.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Global convergence
KW - Nonnegative matrix factorization
KW - One-hot encoding
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85097104359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097104359&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63823-8_40
DO - 10.1007/978-3-030-63823-8_40
M3 - Conference contribution
AN - SCOPUS:85097104359
SN - 9783030638221
T3 - Communications in Computer and Information Science
SP - 342
EP - 350
BT - Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
A2 - Yang, Haiqin
A2 - Pasupa, Kitsuchart
A2 - Leung, Andrew Chi-Sing
A2 - Kwok, James T.
A2 - Chan, Jonathan H.
A2 - King, Irwin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Neural Information Processing, ICONIP 2020
Y2 - 18 November 2020 through 22 November 2020
ER -