Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Compound Classification Models for Recommender Systems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Major components of the gravity recommendation system
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Non-negative matrix factorization on Kernels
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
IEEE Transactions on Signal Processing
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
The link prediction problem in bipartite networks
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
Enhancing matrix factorization through initialization for implicit feedback databases
Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation
Using control theory for stable and efficient recommender systems
Proceedings of the 21st international conference on World Wide Web
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
Discovering latent factors from movies genres for enhanced recommendation
Proceedings of the sixth ACM conference on Recommender systems
Exploiting the characteristics of matrix factorization for active learning in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
A new collaborative filtering approach for increasing the aggregate diversity of recommender systems
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized news recommendation with context trees
Proceedings of the 7th ACM conference on Recommender systems
Understanding and improving relational matrix factorization in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Hybrid recommenders: incorporating metadata awareness into latent factor models
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Boosting the K-Nearest-Neighborhood based incremental collaborative filtering
Knowledge-Based Systems
SCMF: sparse covariance matrix factorization for collaborative filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Elicitation of latent learning needs through learning goals recommendation
Computers in Human Behavior
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Regularized matrix factorization models are known to generate high quality rating predictions for recommender systems. One of the major drawbacks of matrix factorization is that once computed, the model is static. For real-world applications dynamic updating a model is one of the most important tasks. Especially when ratings on new users or new items come in, updating the feature matrices is crucial. In this paper, we generalize regularized matrix factorization (RMF) to regularized kernel matrix factorization (RKMF). Kernels provide a flexible method for deriving new matrix factorization methods. Furthermore with kernels nonlinear interactions between feature vectors are possible. We propose a generic method for learning RKMF models. From this method we derive an online-update algorithm for RKMF models that allows to solve the new-user/new-item problem. Our evaluation indicates that our proposed online-update methods are accurate in approximating a full retrain of a RKMF model while the runtime of online-updating is in the range of milliseconds even for huge datasets like Netflix.