ORIGINAL RESEARCH article

Front. Big Data

Sec. Data Science

Volume 8 - 2025 | doi: 10.3389/fdata.2025.1599704

This article is part of the Research TopicOptimization for Low-rank Data Analysis: Theory, Algorithms and ApplicationsView all 3 articles

Collaborative filtering based on nonnegative/binary matrix factorization

Provisionally accepted
Kazue KudoKazue Kudo1,2*Yukino TeruiYukino Terui1Yuka InoueYuka Inoue1Yohei HamakawaYohei Hamakawa3Kosuke TatsumuraKosuke Tatsumura3
  • 1Ochanomizu University, Bunkyō, Japan
  • 2Tohoku University, Sendai, Miyagi, Japan
  • 3Toshiba (Japan), Tokyo, Japan

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Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. This paper proposes a nonnegative/binary matrix factorization (NBMF) algorithm modified for collaborative filtering and demonstrates that utilizing a low-latency Ising machine in NBMF is advantageous in terms of computation time. While previous studies have primarily applied NBMF to dense data, such as images, this study applies a modified NBMF to sparse data. Results show the benefits of using a low-latency Ising machine to implement the proposed method.

Keywords: Ising Machine, Low-latency, Collaborative Filtering, nonnegative/binary matrix factorization, combinatorial optimization

Received: 24 Apr 2025; Accepted: 11 Jul 2025.

Copyright: © 2025 Kudo, Terui, Inoue, Hamakawa and Tatsumura. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Kazue Kudo, Ochanomizu University, Bunkyō, Japan

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material