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A Hybrid Multi-Strategy Recommender System Using Linked Open Data

Ristoski, Petar and Loza Mencía, Eneldo and Paulheim, Heiko (2014):
A Hybrid Multi-Strategy Recommender System Using Linked Open Data.
In: Semantic Web Evaluation Challenge, Proceedings (ESWC 2014), Springer, pp. 150-156, [Online-Edition: http://2014.eswc-conferences.org/sites/default/files/eswc201...],
[Book Section]

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.

Item Type: Book Section
Erschienen: 2014
Creators: Ristoski, Petar and Loza Mencía, Eneldo and Paulheim, Heiko
Title: A Hybrid Multi-Strategy Recommender System Using Linked Open Data
Language: English
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.

Title of Book: Semantic Web Evaluation Challenge, Proceedings (ESWC 2014)
Series Name: Communications in Computer and Information Science
Volume: 475
Publisher: Springer
ISBN: 978-3-319-12023-2
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Date Deposited: 25 Nov 2015 08:48
Official URL: http://2014.eswc-conferences.org/sites/default/files/eswc201...
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