ACM Transactions on Intelligent Systems and Technology

In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe ...

Probabilistic topic modeling has been applied in a variety of industrial applications. Training a high-quality model usually requires a massive amount of data to provide comprehensive co-occurrence information for the model to learn. However, industrial ...

Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and ...

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the ...

Online data stream mining is of great significance in practice because of its ubiquity in many real-world scenarios, especially in the big data era. Traditional data mining algorithms cannot be directly applied to data streams due to (1) the possible ...

We investigate the problem of community detection in bipartite networks that are characterized by the presence of two types of nodes such that connections exist only between nodes of different types. While some approaches have been proposed to identify ...

Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources ...

Demographic information is important for various commercial and academic proposes, but in reality, few of these data are accessible for analysis and research. To solve this problem, several studies predict demographic attributes from users’ behavioral ...

Context enhancement is critical for the environmental perception in night vision applications, especially for the dark night situation without sufficient illumination. In this article, we propose a thermal image translation method, which can translate ...

Recently, convex-concave bilinear Saddle Point Problems (SPP) is widely used in lasso problems, Support Vector Machines, game theory, and so on. Previous researches have proposed many methods to solve SPP, and present their convergence rate ...

Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which ...

It is known that media outlets, such as CNN and FOX, have intrinsic political bias that is reflected in their news reports. The computational prediction of such bias has broad application prospects. However, the prediction is difficult via directly ...

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to ...