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Semantics-Aware Content-Based Recommender Systems

  • Chapter
Recommender Systems Handbook

Abstract

Content-based recommender systems (CBRSs) rely on item and user descriptions (content) to build item representations and user profiles that can be effectively exploited to suggest items similar to those a target user already liked in the past. Most content-based recommender systems use textual features to represent items and user profiles, hence they suffer from the classical problems of natural language ambiguity. This chapter presents a comprehensive survey of semantic representations of items and user profiles that attempt to overcome the main problems of the simpler approaches based on keywords. We propose a classification of semantic approaches into top-down and bottom-up. The former rely on the integration of external knowledge sources, such as ontologies, encyclopedic knowledge and data from the Linked Data cloud, while the latter rely on a lightweight semantic representation based on the hypothesis that the meaning of words depends on their use in large corpora of textual documents. The chapter shows how to make recommender systems aware of semantics to realize a new generation of content-based recommenders.

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Notes

  1. 1.

    http://babelnet.org/.

  2. 2.

    http://www.w3.org/DesignIssues/LinkedData.html.

  3. 3.

    http://stats.lod2.eu/.

  4. 4.

    http://www.w3.org/TR/rdf-sparql-query/ accessed on September 12, 2014.

  5. 5.

    http://babelfy.org.

  6. 6.

    http://www.alchemyapi.com/.

  7. 7.

    http://www.opencalais.com/.

  8. 8.

    http://www.wikimeta.com/portfolio_nerd.html.

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Acknowledgements

The authors would like to express their deep gratitude to Professor Michael J. Pazzani and Dr. Daniel Billsus for their seminal work on machine learning for user modeling and content-based recommendation systems, which inspired many ideas developed in this chapter.

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  1. Department of Computer Science, University of Bari “Aldo Moro”, Bari, Italy

    Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci & Giovanni Semeraro

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  1. Marco de Gemmis
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Correspondence to Pasquale Lops .

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G. (2015). Semantics-Aware Content-Based Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_4

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