Statistical language models have recently been successfully applied to many information
retrieval problems. A great deal of recent work has shown that statistical language models …

Probabilistic topic models are unsupervised generative models which model document
content as a two-step generation process, that is, documents are observed as mixtures of …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Abstract Representation learning has shown its effectiveness in many tasks such as image
classification and text mining. Network representation learning aims at learning distributed …

Attributed network embedding aims to seek low-dimensional vector representations for
nodes in a network, such that original network topological structure and node attribute …

Embedding network data into a low-dimensional vector space has shown promising
performance for many real-world applications, such as node classification and entity …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …

Although Recommender Systems have been comprehensively analyzed in the past decade,
the study of social-based recommender systems just started. In this paper, aiming at …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …