The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Does Wikipedia Information Help Netflix Predictions?
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
MoviExplain: a recommender system with explanations
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
dbrec: music recommendations using DBpedia
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Recommender Systems Handbook
Linked Data
Top-N recommendations from implicit feedback leveraging linked open data
Proceedings of the 7th ACM conference on Recommender systems
Workshop on recommender systems meet big data & semantic technologies: SeRSy 2013
Proceedings of the 7th ACM conference on Recommender systems
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The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -- both content-based and collaborative filtering ones.