Collaborative filtering based on nonnegative/binary matrix factorization
This work addresses recommendation systems for users by improving matrix factorization on sparse data, but it is incremental as it extends an existing method with modifications.
The paper tackled the problem of collaborative filtering with sparse rating data by proposing a modified nonnegative/binary matrix factorization algorithm that masks unrated entries and uses a low-latency Ising machine, resulting in enhanced prediction accuracy and reduced computation time.
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.