Promoting books and journals to the relevant research communities is an important task for major academic publishers. Unfortunately, identifying which are the best editorial products to market at a certain academic venue is a time-consuming and error-prone process. Here we present the Smart Book Recommender (SBR), an ontology-based recommender that supports the Springer Nature editorial team in selecting the editorial products to market at specific venues. SBR provides an interactive visualisation for analysing the topics characterizing conference series and books. It builds on a dataset of 27K books, journals, and conference proceedings annotated with topics from the Computer Science Ontology, a large-scale ontology of research areas. A user study showed that SBR is able to produce useful recommendations for both editors and researchers.
Major academic publishers, such as Springer Nature, often attend academic conferences and present their most recent books and journals relevant to the conference topics. The purpose of such stands is twofold: the conference participants can browse the most recent research and the authors get additional exposure for their work.
Typically, the selection of products is performed by publishing editors who possess domain specific knowledge and years of experience in publishing books and journals on the conference topics. The process usually requires browsing through a vast catalogue of editorial products on multiple platforms 1,2 . It is a time-consuming and error-prone practice since it is easy to miss some important publications. Furthermore, the constant emergence of new research areas over time poses the challenge of keeping up to date with the research dynamics.
The ongoing collaboration between Springer Nature (SN) and the Knowledge Media
Institute (KMi) of The Open University has given rise to a number of semantic
1 https://link.springer.com
2 https://www.springer.com
technologies to support publishing activities [1]. These solutions include the Smart
Topic Miner3, a tool for classifying proceedings according to a large-scale Computer
Science Ontology, and the Smart Topic API, a web service for assigning semantic
topics to research papers [
The most recent product of this collaboration is the Smart Book Recommender
(SBR) [
The Smart Book Recommender (SBR) is a web application that takes as input a conference series (e.g., “International Semantic Web Conference”) and returns the books, journals, and conference proceedings that are characterized by a set of similar research topics (e.g., the "Handbook of Semantic Web Technologies”). The frontend was implemented using HTML5 and JavaScript, while the backend was developed in Python and queries a MariaDB database.
SBR builds on a dataset of 27K books, journals, and conference proceedings
published by Springer Nature that we annotated with topics from the Computer Science
Ontology (CSO) 4 using the Smart Topic API [
SBR allows users to filter recommendations according to i) publication types (books, journals, or proceedings), ii) publication years, and iii) authors and editors of the books. The publishing editors can also inspect the topic taxonomy of the suggested products, rate the results, and export the list of recommendation, either as a JSON or CSV file. This document is reviewed and sent to the Exhibit Department which takes care of dispatching the publications to the conference.
Figure 2 shows the interactive visualisation offered by SBR for comparing the topics of the input conference series with the ones of a recommended item. This interface intends to provide an intuitive explanation for the similarity score by representing items as taxonomies of topics. It displays in red and blue the topics that appear only in one of the two items and in green the shared ones. The users can also change the granularity of the representation by opting to show only topics that appear in a minimum number of chapters.
We conducted a user study for evaluating SBR involving 7 SN publishing editors and 7 researchers5. These users rated as relevant 76.1% of the first ten recommended items (i.e., precision@10). They also reported that SBR was “intuitive” and “easy to pick up” and that its interface was “simple” and “well-organised”. In particular, editors and the researchers yielded respectively an average SUS score6 of 77.1±15.2 and 80.3±11.3, which converts in a percentile rank of about 75%.
The users also suggested some additional functionalities that we are working to implement, such as the ability to change the topic-based representation of the input conference by adding and removing significant research areas, and the ability to take download statistics into consideration.
In this demo paper we presented our work on the Smart Book Recommender, an ontology-driven recommendation system designed for assisting the Springer Nature publishing editors in selecting books to be marketed at conferences. The semantic representation of research conferences used by SBR was considered very helpful both by editors and researchers, since it allows the users to understand why a certain item was recommended and to compare different products.
As future work, we plan to cover other scientific domains, such as Engineering and Life Science.