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Ontology-Based Recommender Systems

In: Handbook on Ontologies

Author

Listed:
  • Stuart E. Middleton

    (University of Southampton)

  • David De Roure

    (University of Southampton)

  • Nigel R. Shadbolt

    (University of Southampton)

Abstract

Summary We present an overview of the latest approaches to using ontologies in recommender systems and our work on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. In a specific case study we report results from two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. The overall performance of our ontological recommender systems are favourably compared to other systems in the literature.

Suggested Citation

  • Stuart E. Middleton & David De Roure & Nigel R. Shadbolt, 2009. "Ontology-Based Recommender Systems," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 779-796, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-92673-3_35
    DOI: 10.1007/978-3-540-92673-3_35
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    Cited by:

    1. Karamollah Bagherifard & Mohsen Rahmani & Vahid Rafe & Mehrbakhsh Nilashi, 2018. "A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-26, March.

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