Paper 221 (Research track)

Using Ontology-based Data Summarization to Develop Semantics-aware Recommender Systems

Author(s): Tommaso Di Noia, Corrado Magarelli, Andrea Maurino, Matteo Palmonari, Anisa Rula

Abstract: In the current information-centric era, recommender systems are gaining
momentum as tools able to assist users in daily decision-making tasks. They
may exploit users’ past behavior combined with side/contextual information to
suggest them new items or pieces of knowledge they might be interested in.
Within the recommendation process, Linked Data (LD) have been already proposed
as a valuable source of information to enhance the predictive power of
recommender systems not only in terms of accuracy but also of diversity and
novelty of results. In this direction, one of the main open issues in using LD to
feed a recommendation engine is related to feature selection: how to select only
the most relevant subset of the original LD dataset thus avoiding both useless
processing of data and the so called “course of dimensionality” problem. In this
paper we show how ontology-based (linked) data summarization can drive the
selection of properties/features useful to a recommender system. In particular,
we compare a fully automated feature selection method based on ontology-based
data summaries with more classical ones and we evaluate the performance of
these methods in terms of accuracy and aggregate diversity of a recommender
system exploiting the top-k selected features. We set up an experimental testbed
relying on datasets related to different knowledge domains. Results show the feasibility
of a feature selection process driven by ontology-based data summaries
for LD-enabled recommender systems.

Keywords: schema summarization; feature selection; recommender systems

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