Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

While recommendation plays an increasingly critical role in our living, study, work, and
entertainment, the recommendations we receive are often for irrelevant, duplicate, or …

The classification tasks for numerical or categorical data have been well developed.
However, the data collected in the real world are frequently the mixed type containing …

The development of information technology has enabled many different technologies to
collect large amounts of spatial data every day. It is of very great significance to discover …

Complex categorical data is often hierarchically coupled with heterogeneous relationships
between attributes and attribute values and the couplings between objects. Such value-to …

This paper introduces a novel unsupervised outlier detection method, namely Coupled
Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified …

With an explosive growth of data generated in the Internet and other fields, the data stream
classification has sparked broad interest recently. Nowadays, some of the challenges in data …

The representation of categorical data with hierarchical value coupling relationships (ie,
various value-to-value cluster interactions) is very critical yet challenging for capturing …

Generating the precise semantic representation of a word or concept is a fundamental task
in natural language processing. Recent studies which incorporate semantic knowledge into …

Recommender systems have attracted lots of attention since they alleviate the information
overload problem for users. Matrix factorization is one of the most widely employed …