Collaborative filtering based on nonnegative/binary matrix factorization

TL;DR

This paper introduces a modified nonnegative/binary matrix factorization algorithm tailored for sparse collaborative filtering data, leveraging an Ising machine for faster computation and improved prediction accuracy.

Contribution

It proposes a novel adaptation of NBMF for sparse rating data and demonstrates the benefits of using an Ising machine for efficient computation.

Findings

Enhanced prediction accuracy for sparse data

Reduced computation time with Ising machine

Effective handling of unrated items in collaborative filtering

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. 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.