The legal battle over whether prediction markets are a form of gambling has overshadowed something else entirely: They're already a new form of media. Every day, a newsletter from the prediction market Polymarket hits my inbox with a subject line that could easily come from any number of politics-focused newspapers or magazines. Recent entries include "Houthis Reveal New Precision Strike Capabilities," "BREAKING: Trump Unveils Green Energy Beam," and "World War I, 2.0?" These dispatches are roundups with breathless summaries of the day's top stories, linking out to corresponding Polymarket markets--yes, this thing will happen; no, it won't. As far as business news analysis goes, the newsletter is not exactly Bloomberg's, or even Emily Sundberg's --but it's recognizably a commercial editorial product, and one that plays an important role in the prediction market ecosystem.

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.