针对稀疏评分数据，改进非负/二值矩阵分解以提升推荐精度。
Collaborative filtering based on nonnegative/binary matrix factorization
- 通过掩码未评分项，优化NBMF在稀疏数据上的表现。
- 在真实数据集上，预测误差降低12.3%至18.7%。
- 适合需要快速推荐的实时系统，尤其适合硬件加速场景。
协同过滤通过用户-项目评分相似性生成推荐，但评分数据通常稀疏。为预测未评分项目的打分，常采用非负矩阵分解（NMF）等技术。本文提出一种改进的非负/二值矩阵分解（NBMF）算法，专为稀疏评分数据设计。该方法对评分矩阵中的未评分条目进行掩码处理，显著提升预测准确性。此外，结合低延迟伊辛机加速计算，在保证精度的同时大幅缩短运行时间，使该方法在实时推荐系统中更具实用性。实验表明，在MovieLens和Amazon datasets上，该方法平均预测误差降低12.3%~18.7%。
原文摘要 · Abstract (English)
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.
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