Ontology-Based Recommendation of Editorial Products

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.

💡 Research Summary

This paper highlights the importance of automated recommendation systems in the publishing industry. It introduces Smart Book Recommender (SBR), a joint development by Springer Nature and The Open University, aimed at recommending books and journals from among thousands of scholarly publications for specific academic events. SBR leverages the Computer Science Ontology to analyze and recommend approximately 27,000 editorial products. This system addresses the challenges posed by the rapid growth in scientific publications and the dynamic nature of research areas within computer science.

A key feature of SBR is its interactive graphical visualization that allows users to understand why a particular publication was recommended by comparing the topic taxonomy of the recommended product with the event’s thematic focus. This provides editors with valuable information, demonstrating how automated recommendation systems can complement human judgment and enhance efficiency in selecting publications for marketing at specific venues.

The paper presents an evaluation conducted with seven Springer Nature editors and seven OU researchers to assess SBR’s effectiveness. The results confirmed that SBR significantly improves the process of selecting editorial products by providing accurate recommendations tailored to academic events, thereby supporting publishers in their efforts to market relevant content effectively.