Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
What Is an Ontology? Kapitel lesen Erstes Kapitel lesen

2009 | OriginalPaper | Buchkapitel

Ontology-Based Recommender Systems

verfasst von : Stuart E. Middleton, David De Roure, Nigel R. Shadbolt

Erschienen in: Handbook on Ontologies

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Semantic Portals for Cultural Heritage
Literatur
1.
Balabanovic M, Shoham Y (1997) Fab: Content-based, collaborative recommendation. Communications of the ACM 40(3):67–72.CrossRef
2.
Billsus D, Pazzani MJ (1998) A personal news agent that talks, learns and explains. In Autonomous Agents 98, Minneapolis MN, USA.
3.
Burke R (2000) Knowledge-based Recommender Systems. In: Kent A (ed.) Encyclopedia of Library and Information Systems, vol. 69, supplement 32. Marcel Dekker, New York.
5.
Eirinaki M, Lampos C, Paulakis S, Vazirgiannis M (2004) Web personalization integrating content semantics and navigational patterns. In Proceedings of the 6th annual ACM international workshop on Web information and data management, Washington DC, USA.
6.
Felfernig A, Friedrich G, Jannach D, Zanker M (2006) An integrated environment for the development of knowledge-based recommender applications. International Journal of Electronic Commerce 11(2):11–34.CrossRef
7.
Fisher DH (1987) Knowledge acquisition via incremental concept clustering. Machine Learning 2(2):139–172.
8.
Gerhart A (2002) Open directory project search results and ODP status. Search Engine Guide.
9.
Guarino N, Giaretta P (1995) Ontologies and knowledge bases: towards a terminological clarification. In Mars N (ed.) Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing. IOS Press, Amsterdam, pp. 25–32.
10.
Kobsa A (1993) User modeling: recent work, prospects and hazards. In Schneider-Hufschmidt M, Khme T, Malinowski U (ed.) Adaptive User Interfaces: Principles and Practice. Elsevier Amsterdam.
11.
Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997) GroupLens: applying collaborative filtering to usenet news. Communications of the ACM 40(3):77–87.CrossRef
12.
Lang K (1995) NewsWeeder: learning to filter NetNews. In ICML95 Conference Proceedings, pp. 331–339.
13.
Lee T, Chun J, Shim J, Lee S (2006) An ontology-based product recommender system for B2B marketplaces. International Journal of Electronic Commerce 11(2):125–155.CrossRef
14.
Linden G, Smith B, York J (2003) Amazon.com recommendations: Item-to-Item collaborative filtering. IEEE Internet Computing 7(1):76–80.CrossRef
15.
Mccallum AK, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Information Retrieval 3(2):127–163.CrossRef
16.
Middleton SE, Alani H, Shadbolt NR, De Roure DC (2002) Exploiting synergy between ontologies and recommender systems. In International Workshop on the Semantic Web, Proceedings of the 11th International World Wide Web Conference WWW-2002, Hawaii, USA.
17.
Middleton SE, De Roure DC, Shadbolt NR (2001) Capturing knowledge of user preferences: ontologies on recommender systems. In Proceedings of the First International Conference on Knowledge Capture K-CAP 2001, Victoria, BC, Canada.
18.
Middleton SE, Shadbolt NR, De Roure DC (2004) Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS) 22(1):54–88, ACM Press, New York.CrossRef
19.
Schickel-Zuber V, Faltings B (2007) Using hierarchical clustering for learning the ontologies used in recommendation systems. In KDD 2007, California, USA.
20.
Sebastiani F (2002) Machine learning in automated text categorization. ACM Computing Surveys 34(1):1–47.CrossRef
21.
Szomszor M, Cattuto C, Alani H, O’Hara K, Baldassarri A, Loreto V, Servedio VDP (2007) Folksonomies, the Semantic Web, and Movie Recommendation. In Proceedings of 4th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0 (in press), Innsbruck, Austria.
22.
Zhao Y, Karypis G (2005) Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery 10:141–168.MathSciNetCrossRef
23.
Ziegler C, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, Chiba, Japan.
Metadaten
Titel
Ontology-Based Recommender Systems
verfasst von
Stuart E. Middleton
David De Roure
Nigel R. Shadbolt
Copyright-Jahr
2009
Verlag
Springer Berlin Heidelberg
Buch
Handbook on Ontologies

Print ISBN: 978-3-540-70999-2

Electronic ISBN: 978-3-540-92673-3

Copyright-Jahr: 2009

DOI
https://doi.org/10.1007/978-3-540-92673-3_35