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Targeting more relevant, contextual recommendations by exploiting domain knowledge

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HetRec '10: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems

Pages 57 - 62

https://doi.org/10.1145/1869446.1869455

Published: 26 September 2010 Publication History

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Abstract

In today's mobile applications, it becomes more and more important to have a broader view on knowledge about a certain domain when generating contextual and semantic recommendations. Data that provides additional and useful information to the traditional User x Item representation, such as taxonomies, implicit and indirect knowledge about a user's preferences or location information can immensely enhance the quality of recommendations. For this purpose, the generic recommender system of Fraunhofer Institute FOKUS, the SMART Recommendations Engine, has been extended by the SMART Ontology Extension and the Proximity Filter, which enable the recommender to use domain knowledge included in semantic ontologies and contextual information in the recommendation process in order to generate much more precise recommendations. The functionality of the extensions are demonstrated in the scope of a food purchase scenario.

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Cited By

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Tomkins SIsley SLondon BGetoor LPera SEkstrand MAmatriain XO'Donovan J(2018)Sustainability at scaleProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240411(214-218)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240411
Ben Sassi IMellouli SBen Yahia S(2017)Context-aware recommender systems in mobile environmentInformation Systems10.1016/j.is.2017.09.00172:C(27-61)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.is.2017.09.001
Mettouris CPapadopoulos G(2015)Designing Context Models for CARS Incorporating Partially Observable ContextModeling and Using Context10.1007/978-3-319-25591-0_9(118-131)Online publication date: 15-Dec-2015
https://doi.org/10.1007/978-3-319-25591-0_9
Show More Cited By

Index Terms

Targeting more relevant, contextual recommendations by exploiting domain knowledge
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

HetRec '10: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems

September 2010

84 pages

ISBN:9781450304078

DOI:10.1145/1869446

Conference Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Iván Cantador
Universidad Autónoma de Madrid, Spain
,
Yehuda Koren
Yahoo! Labs
,
Tsvi Kuflik
University of Haifa, Israel
,
Markus Weimer
Yahoo! Labs

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Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26, 2010

Barcelona, Spain

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Tomkins SIsley SLondon BGetoor LPera SEkstrand MAmatriain XO'Donovan J(2018)Sustainability at scaleProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240411(214-218)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240411
Ben Sassi IMellouli SBen Yahia S(2017)Context-aware recommender systems in mobile environmentInformation Systems10.1016/j.is.2017.09.00172:C(27-61)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.is.2017.09.001
Mettouris CPapadopoulos G(2015)Designing Context Models for CARS Incorporating Partially Observable ContextModeling and Using Context10.1007/978-3-319-25591-0_9(118-131)Online publication date: 15-Dec-2015
https://doi.org/10.1007/978-3-319-25591-0_9
Mettouris CPapadopoulos G(2013)Contextual Modelling in Context-Aware Recommender Systems: A Generic ApproachWeb Information Systems Engineering – WISE 2011 and 2012 Workshops10.1007/978-3-642-38333-5_6(41-52)Online publication date: 2013
https://doi.org/10.1007/978-3-642-38333-5_6

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