Collaborative filtering based on nonnegative/binary matrix factorization
Published in arXiv (Cornell University) • Oct 14, 2024
NobleIDNI7P85W89R30S66
Authors:,,
Yukino Terui
Yukari Inoue
Yohei Hamakawa
Abstract
Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix factorization techniques such as nonnegative matrix factorization (NMF) are often employed. Nonnegativ...
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