Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that
has been successfully exploited in various applications, but sometimes they fail to provide …

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor
matrices to obtain an optimal low-rank representation feature for the High-Order, High …

With the boom in the development of computer hardware and software, social media, IoT
platforms, and communications, there has been exponential growth in the volume of data …

Sparse tensor decomposition, such as Canonical Polyadic Decomposition (CPD), is a key
operation for data analytics and machine learning. Its computation is dominated by a set of …

High-order, high-dimension, and large-scale sparse tensors (HHLST) have found their origin
in various real industrial applications, such as social networks, recommender systems …

The amount of scientific data is currently growing at an unprecedented pace, with tensors
being a common form of data that display high-order, high-dimensional, and sparse …

Abstract Non-negative Tensor Factorization (NTF) models are effective and efficient in
extracting useful knowledge from various types of probabilistic distribution with multi-way …

Tensors are naturally suitable for representing high-dimensional data. Tensor train
decomposition is an effective data processing method to cope with high-dimensional …