=Paper=
{{Paper
|id=Vol-1963/paper585
|storemode=property
|title=Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products
|pdfUrl=https://ceur-ws.org/Vol-1963/paper585.pdf
|volume=Vol-1963
|authors=Francesco Osborne,Thiviyan Thanapalasingam,Angelo Antonio Salatino,Aliaksandr Birukou,Enrico Motta
|dblpUrl=https://dblp.org/rec/conf/semweb/OsborneTSBM17
}}
==Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products==
https://ceur-ws.org/Vol-1963/paper585.pdf
Smart Book Recommender: A Semantic Recommendation
Engine for Editorial Products
Francesco Osborne1, Thiviyan Thanapalasingam1, Angelo Salatino1,
Aliaksandr Birukou2, Enrico Motta1
1
Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK
{francesco.osborne, thiviyan.thanapalasingam,
angelo.salatino, enrico.motta}@open.ac.uk
2
Springer-Verlag GmbH, Tiergartenstrasse 17, 69121 Heidelberg, Germany
aliaksandr.birukou@springer.com
Abstract. Academic publishers, such as Springer Nature, need to constantly make
informed decisions about how and where to market their editorial products. In the
field of Computer Science (CS), it is particularly critical to assess which books will
be of interest to the attendees of a conference. Typically, these items are manually
chosen by publishing editors, on the basis of their personal experience. To make this
process both faster and more robust we have developed the Smart Book
Recommender (SBR), a semantic application designed to support the Springer
Nature editorial team in promoting their publications at CS venues. SBR takes as
input the proceedings of a conference and suggests books, journals, and other
conference proceedings which are likely to be relevant to the attendees of the
conference in question. It does so by taking advantage of a semantic representation
of topics, which builds on a very large ontology of Computer Science topics;
characterizing Springer Nature books as distributions of semantic topics; and
approaching the problem as one of semantic matching between such distributions of
semantic topics.
Keywords: Scholarly Data, Recommendation Systems, Ontology, Bibliographic
Data, Scholarly Ontologies.
1 Introduction
Academic publishers need to constantly make timely and data-driven decisions to ensure
that they are showcasing their editorial products to their target market. In the field of
Computer Science, it is particularly critical to assess which books, journal, or proceedings
will be of interest for the attendees of a conference. Typically, these items are manually
chosen by publishing editors, on the basis of their personal experience. As the number of
publications grows, there is an increasing need for automated and data-driven methods
that can support this complex and time-consuming task by analysing large-scale data
about editorial products.
In what follows we present the Smart Book Recommender (SBR), a web application
developed in collaboration with Springer Nature, which recommends books, journals and
conference proceedings that are likely to be relevant to the attendees of a given
conference. This work stems from the ongoing collaboration between Springer Nature and
the Knowledge Media Institute (KMi) of the Open University, which has produced a
number of other innovative solutions, including Smart Topic Miner (STM) [1], a semantic
framework for classifying academic documents, and its API, the Smart Topic API.
Since January 2017, STM is being routinely used by the SN Computer Science editorial
team, halving the time for classifying conference proceeding. Similarly, SBR is in line to
be adopted to support SN editors in selecting the best set of books to market to the
participants of a conference. A demo of the SBR prototype is available at
http://rexplore.kmi.open.ac.uk/SBR-demo.
Figure 1. The SBR interface.
2 Smart Book Recommender
SBR takes as input the title of a SN book, usually the proceedings of a conference, and
returns a list of books, journals, and proceedings which address topics that are likely to be
relevant to the participants of the conference in question. To do so, it represents SN books
in Computer Science as distributions of semantically-characterized topics, which are
drawn from a large-scale ontology of Computer Science, and then computes their pairwise
similarity. SBR offers a simple web interface, shown in Figure 1, to allow editors to filter
the results and share their feedback. It is thus complementary, but very different in scope,
to Reccomended1, the SN recommender system, which suggest books to users on the basis
of their last 100 papers read on online platforms.
SBR relies on the following background knowledge: a large database of SN book
metadata and the Computer Science Ontology (CSO). The database of metadata contains
titles, abstracts, keywords and other information describing the chapters of about 27K
books and 270 journals in the field of Computer Science. In the case of conference
proceedings and journals, each chapter is usually a research paper.
CSO is a large scale and granular ontology of research topics that has been created
automatically by running the Klink-2 algorithm [2] on the Rexplore dataset [3], which
consists of about 16 million publications in the field of Computer Science. It is an
extension2 of the BIBO ontology3 which in turn builds on SKOS. The current version of
CSO includes about 17K topics, which are linked by 70K semantic relationships.
1
http://recommended.springernature.com/recommended/
2
http://kmi.open.ac.uk/technologies/rexplore/ontologies/BiboExtension.owl
2.1 Architecture
Figure 2 shows the architecture of SBR. The computation of the pairwise similarity
between SN books is performed offline. The Recommendation Engine iterates on
journals, conference proceedings, and other books, and retrieves for each of them the
relevant set of chapters/papers. It then sends this metadata to the Smart Topic API, which
extracts frequent terms from abstracts, titles and keywords, maps them to the CSO
ontology concepts, and prunes the resulting topics with a set-covering algorithm as
detailed in [1]. The mappings from terms to concepts take into account both synonyms
and sub-areas of a topic – e.g., all documents associated to terms such as “semantic
technologies”, “linked data”, “RDF”, “OWL” will be also tagged with the topic “Semantic
Web”. The result of this process is a distribution of topics from CSO, in which each topic
is associated with the number of chapters/papers addressing it. The recommendation
engine uses this representation for pre-computing and storing in the database the similarity
scores. Presently, the prototype uses the cosine similarity of the topic vectors, but we are
testing other similarity metrics.
Figure 2. The architecture of SBR.
Since computing the cosine similarity of all the books in the dataset is computationally-
heavy, we consider only promising pairs which obtain a jaccard similarity of at least
0.125. A data analysis revealed that this heuristic halves the number of candidate pairs
while still producing very good results.
When the user submits an input book to the SBR web interface, the relevant ID and user
settings are sent as JSON to the background API via a GET query. The API queries the
database for selecting the most similar books and returns their descriptions. Both the API
and the recommendation engine are realized in Python.
2.2 The Web Interface
The user can select specific conference proceedings by typing their name in an
autocomplete field. The recommendation results are displayed in order of descending
similarity scores and they can be filtered according to document types (journals, books,
conference proceedings) and year range.
Figure 3 shows an example of proceedings suggested by SBR. For simplicity,
proceedings of different editions of the same conferences are grouped together. Each
proceedings volume is described according to its title, year and the top fifteen topics. SBR
also highlights in blue the topics which are among the top fifty topics of the input book.
The user has the option to provide feedback about each item using emoticons buttons.
When the user clicks either button, the feedback is sent to the API and recorded in the
3
http://purl.org/ontology/bibo/
database. These data will be used to determine the quality of different similarity metrics
and further enhance the recommendation process.
Figure 3. Example of suggested conference proceedings.
3 Conclusions
In this demo paper, we presented the prototype of SBR, a novel system for identifying
related editorial products and facilitating the marketing process at SN.
As next steps, we intend to improve the recommendation process using other features
(e.g., sales figures) and to conduct a formal evaluation with a group of SN editors. We are
also planning to design a more advanced user interface for comparing the topics of
different books and to implement a new version of the system for assisting researchers in
identifying books and conferences which are relevant to their work.
4 References
1. Osborne, F., Salatino, A., Birukou, A., Motta, E.: Automatic Classification of Springer Nature
Proceedings with Smart Topic Miner. In International Semantic Web Conference 2016 (pp.
383-399). Springer. (2016)
2. Osborne, F., Motta, E.: Klink-2: integrating multiple web sources to generate semantic topic
networks. In International Semantic Web Conference 2015 (pp. 408-424). Springer. (2015)
3. Osborne, F., Motta, E. and Mulholland, P.: Exploring scholarly data with Rexplore. In
International Semantic Web Conference 2013 (pp. 460-477). Springer. (2013)