Exploiting frameNet for content-based book recommendation

De Clercq, Orphée ; Schuhmacher, Michael ; Ponzetto, Simone Paolo ; Hoste, Veronique

URL: http://ceur-ws.org/Vol-1245/cbrecsys2014-paper03.p...
Additional URL: http://ceur-ws.org/Vol-1245/cbrecsys2014-proceedin...
Document Type: Conference or workshop publication
Year of publication: 2014
Book title: CBRecSys 2014 New Trends in Content-based Recommender Systems : Proceedings of the 1st Workshop on New Trends in Content-based Recommender Systems co-located with the 8th ACM Conference on Recommender Systems (RecSys 2014)
The title of a journal, publication series: CEUR Workshop Proceedings
Volume: 1245
Page range: 14-21
Conference title:
Location of the conference venue: Foster City, Silicon Valley, Calif.
Date of the conference: October 6, 2014
Publisher: Bogers, Toine
Place of publication: Aachen, Germany
Publishing house: RWTH Aachen
ISSN: 1613-0073
Publication language: English
Institution: School of Business Informatics and Mathematics > Semantic Web (Juniorprofessur) (Ponzetto 2013-2015)
Subject: 004 Computer science, internet
Abstract: Adding semantic knowledge to a content-based recommender helps to better understand the items and user representations. Most recent research has focused on examining the added value of adding semantic features based on structured web data, in particular Linked Open Data (LOD). In this paper, we focus in contrast on semantic feature construction from text, by incorporating features based on semantic frames into a book recommendation classifier. To this purpose we leverage the semantic frames based on parsing the plots of the items under consideration with a state-of-the-art semantic parser. By investigating this type of semantic information, we show that these frames are also able to represent information about a particular book, but without the need of having explicitly structured data describing the books available. We reveal that exploiting frame information outperforms a basic bag-of-words approach and that especially the words relating to those frames are beneficial for classification. In a final step we compare and combine our system with the LOD features from a system leveraging DBpedia as knowledge resource. We show that both approaches yield similar results and reveal that combining semantic information from these two different sources might even be beneficial.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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