Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …

Reliable data measurement is considered to be one of the critical ingredients for variant
Internet of Things (IoT) applications. Gaining full knowledge of measurement data is …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …

In the realm of personalized recommender systems, the challenge of adapting to evolving
user preferences and the continuous influx of new users and items is paramount …

Low-rank tensor factorization has been widely used for many real world tensor completion
problems. While most existing factorization models assume a multilinearity relationship …

With the continuous increase of users and items, conventional recommender systems
trained on static datasets can hardly adapt to changing environments. The high-throughput …

Practical tensor data is often along with time information. Most existing temporal
decomposition approaches estimate a set of fixed factors for the objects in each tensor …

Given a dense tensor, how can we efficiently discover hidden relations and patterns in static
and online streaming settings? Tucker decomposition is a fundamental tool to analyze …

Techniques for using multi-way traffic patterns for traffic prediction is gaining importance.
One of the possible techniques for representing the multi-way traffic patterns is tensors …