Détail du document
Identifiant
oai:arXiv.org:2410.10381
Sujet
Condensed Matter - Statistical Mec... Computer Science - Information Ret... Computer Science - Machine Learnin...Auteur
Terui, Yukino Inoue, Yuka Hamakawa, Yohei Tatsumura, Kosuke Kudo, KazueCatégorie
Computer Science
Année
2024
Date de référencement
01/01/2025
Mots clés
items nonnegative nbmf factorizationRésumé
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
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