# Collaborative filtering based on nonnegative/binary matrix factorization

arXiv: https://arxiv.org/abs/2410.10381 · HTML: https://mlsanity.com/paper/2410.10381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Support vector machines on the D-Wave quantum annealer](https://mlsanity.com/paper/1906.06283/) (2019)
- [Nonnegative/binary matrix factorization with a D-Wave quantum annealer](https://mlsanity.com/paper/1704.01605/) (2017)
- [Reverse Annealing for Nonnegative/Binary Matrix Factorization](https://mlsanity.com/paper/2007.05565/) (2020)
- [A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications](https://mlsanity.com/paper/2106.02964/) (2021)
- [Correlation-diversified portfolio construction by finding maximum independent set in large-scale market graph](https://mlsanity.com/paper/2308.04769/) (2023)
- [Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing](https://mlsanity.com/paper/2311.01028/) (2023)
- [Image Analysis Based on Nonnegative/Binary Matrix Factorization](https://mlsanity.com/paper/2007.00889/) (2020)

## Abstract (arXiv)

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. Nonnegative/binary matrix factorization (NBMF), which is an extension of NMF, approximates a nonnegative matrix as the product of nonnegative and binary matrices. While previous studies have applied NBMF primarily to dense data such as images, this paper proposes a modified NBMF algorithm tailored for collaborative filtering with sparse data. In the modified method, unrated entries in the rating matrix are masked, enhancing prediction accuracy. Furthermore, utilizing a low-latency Ising machine in NBMF is advantageous in terms of the computation time, making the proposed method beneficial.