Popular Books and Linked Data: Some Results for the ESWC’14 RecSys Challenge

Schuhmacher, Michael ; Meilicke, Christian

DOI: https://doi.org/10.1007/978-3-319-12024-9_23
URL: http://2014.eswc-conferences.org/sites/default/fil...
Document Type: Conference or workshop publication
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: 176-181
Date of the conference: 25.05.2014
Publisher: Presutti, Valentina
Place of publication: Berlin [u.a.]
Publishing house: Springer
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 > Semantic Web (Juniorprofessur) (Ponzetto 2013-2015)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Within this paper we present our contribution to Task 2 of the ESWC’14 Recommender Systems Challenge. First we describe an unpersonalized baseline approach that uses no linked-data but applies a naive way to compute the overall popularity of the items observed in the training data. Despite being very simple and unpersonalized, we achieve a competitive F1 measure of 0.5583. Then we describe an algorithm that makes use of several features acquired from DBpedia, like author and type, and self-generated features like abstract-based keywords, for item representation and comparison. Item recommendations are generated by a mixture-model of individual classifiers that have been learned per feature on a user neighborhood cluster in combination with a global classifier learned on all training data. While our Linked-Data-based approach achieves an F1 measure of 0.5649, the increase over the popularity baseline remains surprisingly low.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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