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Using Ontology-Based Data Summarization to Develop Semantics-Aware Recommender Systems

  • Tommaso Di Noia
  • Corrado Magarelli
  • Andrea Maurino
  • Matteo Palmonari
  • Anisa Rula
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)

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 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 Linked Data to feed a recommendation engine is related to feature selection: how to select only the most relevant subset of the original Linked Data thus avoiding both useless processing of data and the so called “curse 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 Linked Data-enabled recommender systems.

Notes

Acknowledgments

This research has been supported in part by EU H2020 projects EW-Shopp - Grant n. 732590, and EuBusinessGraph - Grant n. 732003.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Polytechnic University of BariBariItaly
  2. 2.University of Milano-BicoccaMilanItaly
  3. 3.University of BonnBonnGermany

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