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

Traditional stock market prediction approaches commonly utilize the historical price-related
data of the stocks to forecast their future trends. As the Web information grows, recently …

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

Complex applications such as big data analytics involve different forms of coupling
relationships that reflect interactions between factors related to technical, business (domain …

We employ multiple heterogeneous data sources, including historical transaction data,
technical indicators, stock posts, news and Baidu index, to predict the directions of stock …

Most of the classic theoretical systems and tools in statistics, data mining and machine
learning are built on the fundamental assumption of IIDness, which assumes the …

Rough set theory is an essential tool for measuring uncertainty, which has been widely
applied in attribute reduction algorithms. Most of the related researches focus on how to …

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

Recommender system has caught much attention from multiple disciplines, and many
techniques are proposed to build it. Recently, social recommendation becomes a hot …

Many state of the art methods analyze sentiments in news to predict stock price. When
predicting stock price movement, the correlation between stocks is a factor that can't be …