Abstract

This paper discusses the combination of collaborative and content-based 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).

@inproceedings{spiegel2009a,
title = {Hydra: a hybrid recommender system [cross-linked rating and content information]},
author = {Stephan Spiegel and Jérôme Kunegis and Fang Li},
booktitle = {Proc. Workshop on Complex Networks in Information and Knowledge Management}, 
doi = {10.1145/1651274.1651289},
isbn = {978-1-60558-807-0},
keywords = {recommender},
location = {Hong Kong, China},
pages = {75--80},
publisher = {ACM},
url = {http://dx.doi.org/10.1145/1651274.1651289},
year = {2009},
}