A Hybrid Multi-strategy Recommender System Using Linked Open Data

Ristoski, Petar ; Loza Mencía, Eneldo ; Paulheim, Heiko

Ristoski_et_al_-_A_Hybrid_Multi-Strategy_Recommender_System_Using_Linked_Open_Data.pdf - Published

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URL: https://madoc.bib.uni-mannheim.de/37099
Additional URL: http://2014.eswc-conferences.org/sites/default/fil...
URN: urn:nbn:de:bsz:180-madoc-370991
Document Type: Book chapter
Year of publication: 2014
Book title: Semantic Web Evaluation Challenge : SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers
The title of a journal, publication series: Communications in Computer and Information Science
Volume: 475
Page range: 150-156
Publisher: Presutti, Valentina
Place of publication: Cham
Publishing house: Springer Internat. Publ.
ISBN: 978-3-319-12023-2 , 978-3-319-12024-9
ISSN: 1865-0929 , 1865-0937
Publication language: English
Institution: School of Business Informatics and Mathematics > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
School of Business Informatics and Mathematics > Wirtschaftsinformatik V (Bizer)
Subject: 004 Computer science, internet
Keywords (English): Linked Open Data , recommender
Abstract: In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.

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