This timely book presents Applications in Recommender Systems which are making recommendations
using machine learning algorithms trained via examples of content the user likes or dislikes.
Recommender systems built on the assumption of availability of both positive and negative
examples do not perform well when negative examples are rare. It is exactly this problem that
the authors address in the monograph at hand. Specifically the books approach is based on
one-class classification methodologies that have been appearing in recent machine learning
research. The blending of recommender systems and one-class classification provides a new very
fertile field for research innovation and development with potential applications in big data
as well as sparse data problems.The book will be useful to researchers practitioners and
graduate students dealing with problems of extensive and complex data. It is intended for both
the expert researcher in the fields of Pattern Recognition Machine Learning and Recommender
Systems as well as for the general reader in the fields of Applied and Computer Science who
wishes to learn more about the emerging discipline of Recommender Systems and their
applications. Finally the book provides an extended list of bibliographic references which
covers the relevant literature completely.
