Hydra: a hybrid recommender system [cross-linked rating and content information]

This paper discusses the combination of collaborative and contentbased filtering in the context of web-based recommender systems. In particular, we link the well-known MovieLens rating data with supplementary IMDB content information. The resulting network of user-item relations and associated content features is converted into a unified mathematical model, which is applicable to our underlying neighbor-based prediction algorithm. By means of various experiments, we demonstrate the influence of supplementary user as well as item features on the prediction accuracy of Hydra, our proposed hybrid recommender. In order to decrease system runtime and to reveal latent user and item relations, we factorize our hybrid model via singular value decomposition (SVD). Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information filtering; H.3.4 [Information Storage and Retrieval]: Systems and Software—Information networks Genera...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Stephan Spiegel, Jérôme Kunegis, Fang Li
• Keywords: CIKM 2009, Database, Hybrid Recommender, Keywords Hybrid Recommender, Web-based Recommender Systems