Collaborative filtering based on nonnegative/binary matrix factorization

Document detail

ID

oai:arXiv.org:2410.10381

Topic

Condensed Matter - Statistical Mec... Computer Science - Information Ret... Computer Science - Machine Learnin...

Author

Terui, Yukino Inoue, Yuka Hamakawa, Yohei Tatsumura, Kosuke Kudo, Kazue

Year

2024

listing date

1/1/2025

Keywords

items nonnegative nbmf factorization

Metrics

Abstract

Collaborative filtering generates recommendations based on user-item similarities through rating data, which may involve numerous unrated items.

To predict scores for unrated items, matrix factorization techniques, such as nonnegative matrix factorization (NMF), are often employed to predict scores for unrated items.

Nonnegative/binary matrix factorization (NBMF), which is an extension of NMF, approximates a nonnegative matrix as the product of nonnegative and binary matrices.

Previous studies have employed NBMF for image analysis where the data were dense.

In this paper, we propose a modified NBMF algorithm that can be applied to collaborative filtering where data are sparse.

In the modified method, unrated elements in a rating matrix are masked, which improves the collaborative filtering performance.

Utilizing a low-latency Ising machine in NBMF is advantageous in terms of the computation time, making the proposed method beneficial.

;Comment: 14 pages, 7 figures

Terui, Yukino,Inoue, Yuka,Hamakawa, Yohei,Tatsumura, Kosuke,Kudo, Kazue, 2024, Collaborative filtering based on nonnegative/binary matrix factorization