HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems

Abstract

As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender system… (More)
DOI: 10.1145/2792838.2800175

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Cite this paper

@inproceedings{Kouki2015HyPERAF, title={HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems}, author={Pigi Kouki and Shobeir Fakhraei and James R. Foulds and Magdalini Eirinaki and Lise Getoor}, booktitle={RecSys}, year={2015} }