Hybrid Recommending Exploiting Multiple DBPedia Language Editions

Abstract

In this paper we describe approach of our SemWex1 group to the ESWC 2014 RecSys Challenge. Our method is based on using an adaptation of Content Boosted Matrix factorization [1], where objects are defined through their content-based features.

Features were comprised of both direct DBPedia RDF triples and derived semantic information (with some WIE and NLP features). Total of seven DBPedia language editions were used to form the dataset.

In the paper we will further describe our methods for semantic information creation, data filtration, algorithm details and settings as well as decisions made during the challenge and dead ends we explored.

Keywords

• Hybrid recommender systems
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  Linked open data
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  DBPedia
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  Matrix factorization
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  Content boosting