Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

Graphical models have become the basic framework for topic based probabilistic modeling.
Especially models with latent variables have proved to be effective in capturing hidden …

Abstract Probabilistic Latent Semantic Analysis is a novel statistical technique for the
analysis of two {mode and co-occurrence data, which has applications in information …

This paper presents a novel statistical method for factor analysis of binary and count data
which is closely related to a technique known as Latent Semantic Analysis. In contrast to the …

We propose to solve the link prediction problem in graphs using a supervised matrix
factorization approach. The model learns latent features from the topological structure of a …

In this paper we present methods of enhancing existing discriminative classifiers for multi-
labeled predictions. Discriminative methods like support vector machines perform very well …

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted
entirely to techniques for producing knowledge from the vast body of unstructured Web data …

A large portion of real-world data is stored in commercial relational database systems. In
contrast, most statistical learning methods work only with “flat” data representations. Thus, to …

Motivated by genetic expression data, we introduce plaid models. These are a form of two-
sided cluster analysis that allows clusters to overlap. Plaid models also incorporate additive …

Preference data is a form of dyadic data, with measurements associated with pairs of
elements arising from two discrete sets of objects. These are users and items, as well as …