TU Darmstadt / ULB / TUbiblio

A Hybrid Multi-Strategy Recommender System Using Linked Open Data

Ristoski, Petar ; Loza Mencía, Eneldo ; Paulheim, Heiko (2014)
A Hybrid Multi-Strategy Recommender System Using Linked Open Data.
In: Semantic Web Evaluation Challenge, Proceedings (ESWC 2014)
Buchkapitel, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags: Buchkapitel
Erschienen: 2014
Autor(en): Ristoski, Petar ; Loza Mencía, Eneldo ; Paulheim, Heiko
Art des Eintrags: Bibliographie
Titel: A Hybrid Multi-Strategy Recommender System Using Linked Open Data
Sprache: Englisch
Publikationsjahr: 2014
Verlag: Springer
Buchtitel: Semantic Web Evaluation Challenge, Proceedings (ESWC 2014)
Reihe: Communications in Computer and Information Science
Band einer Reihe: 475
URL / URN: http://2014.eswc-conferences.org/sites/default/files/eswc201...
Kurzbeschreibung (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.

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Knowledge Engineering
Hinterlegungsdatum: 25 Nov 2015 08:48
Letzte Änderung: 25 Nov 2015 08:48
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen