Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Content-based recommendation systems
The adaptive web
Hybrid web recommender systems
The adaptive web
Bridging the gap: complex networks meet information and knowledge management
Proceedings of the 18th ACM conference on Information and knowledge management
Pattern recognition and classification for multivariate time series
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Pattern recognition in multivariate time series: dissertation proposal
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Link prediction on evolving data using tensor factorization
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Geo-activity recommendations by using improved feature combination
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Future Generation Computer Systems
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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).