Sparse matrix vector multiplication (SpMV) is one of the most common operations in
scientific and high-performance applications, and is often responsible for the application …

Sparse matrix–vector multiplications (SpMV) are common in scientific and HPC applications
but are hard to be optimized. While the ARMv8-based processor IP is emerging as an …

Alternating least squares (ALS) has been proved to be an effective solver for matrix
factorization in recommender systems. To speed up factorizing performance, various parallel …

Collaborative filtering is one of the most extensively utilized recommendation algorithms in
the e-commerce industry. It typically relies either on implicit or explicit feedback. The existing …

The use of the latent factor models technique overcomes two major problems of most
collaborative filtering approaches: scalability and sparseness of the user's profile matrix. The …

Matrix factorization on sparse matrices has been proven to be an effective approach for data
mining and machine learning. However, the prior parallel implementations for matrix …

The recommendation system has been a vital study topic in recent years, so many scientists
and academics across the world are interested in researching the subject. Music, movies …

Sparse matrix vector multiplication (SpMV) is one of the most common operations in
scientific and high-performance applications, and is often responsible for the application …

Matrix factorization is a basis for many recommendation systems. Although alternating least
squares with weighted-λ-regularization (ALS-WR) is widely used in matrix factorization with …

Data characterized by high dimensionality and sparsity are commonly used to describe real-
world node interactions. Low-rank representation (LR) can map high-dimensional sparse …