  Major academic publishers need to be able to analyse their vast catalogue of
products and select the best items to be marketed in scientific venues. This is
a complex exercise that requires characterising with a high precision the
topics of thousands of books and matching them with the interests of the
relevant communities. In Springer Nature, this task has been traditionally
handled manually by publishing editors. However, the rapid growth in the number
of scientific publications and the dynamic nature of the Computer Science
landscape has made this solution increasingly inefficient. We have addressed
this issue by creating Smart Book Recommender (SBR), an ontology-based
recommender system developed by The Open University (OU) in collaboration with
Springer Nature, which supports their Computer Science editorial team in
selecting the products to market at specific venues. SBR recommends books,
journals, and conference proceedings relevant to a conference by taking
advantage of a semantically enhanced representation of about 27K editorial
products. This is based on the Computer Science Ontology, a very large-scale,
automatically generated taxonomy of research areas. SBR also allows users to
investigate why a certain publication was suggested by the system. It does so
by means of an interactive graph view that displays the topic taxonomy of the
recommended editorial product and compares it with the topic-centric
characterization of the input conference. An evaluation carried out with seven
Springer Nature editors and seven OU researchers has confirmed the
effectiveness of the solution.
