Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

Two-way arrays or matrices are often not enough to represent all the information in the data
and standard two-way analysis techniques commonly applied on matrices may fail to find …

Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …

This paper proposes a novel approach to tensor completion, which recovers missing entries
of data represented by tensors. The approach is based on the tensor train (TT) rank, which is …

The topic of semantic segmentation has witnessed considerable progress due to the
powerful features learned by convolutional neural networks (CNNs). The current leading …

Web search is an integral part of our daily lives. Recently, there has been a trend of
personalization in Web search, where different users receive different results for the same …

This survey provides an overview of higher-order tensor decompositions, their applications,
and available software. A tensor is a multidimensional or-way array. Decompositions of …

In recent years, interest in recommender research has shifted from explicit feedback towards
implicit feedback data. A diversity of complex models has been proposed for a wide variety …

Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for
multilinear data analysis against outliers, gross corruptions, and missing values and has a …

Higher-order low-rank tensors naturally arise in many applications including hyperspectral
data recovery, video inpainting, seismic data recon-struction, and so on. We propose a new …