Many data mining tasks operate on dyadic data, i.e., data involving two types of entities (e.g., users and products, objects and attributes, points and coordinates, or vertices in a graph). Such dyadic data can be naturally represented in terms of a matrix, which opens up a range of powerful data mining techniques. This course provides an introduction into matrix decomposition models and algorithms for analyzing dyadic data, covers data mining tasks such as prediction, clustering, pattern mining, and dimensionality reduction, as well as application areas such as recommender systems, information retrieval, information extraction, and topic modelling.
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