IEEE Transactions on Knowledge and Data Engineering
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
A Log-Linear Model with Latent Features for Dyadic Prediction
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender Systems Handbook
Large-scale matrix factorization with distributed stochastic gradient descent
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
gSVD++: supporting implicit feedback on recommender systems with metadata awareness
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Hybrid recommenders: incorporating metadata awareness into latent factor models
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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Current approaches on collaborative filtering factorize user-item matrices in order to infer latent factors from ratings previously assigned by users. However, they all have to deal with sparseness, whose workarounds are prone to bias and/or overfitting. This paper proposes a recommender algorithm that is based on a factorized matrix composed of user preferences associated to the movies' genres/categories. The advantage of using such user-genre matrix factorization model is that it requires less computational resources, as the matrix will be less sparse and at lower dimension. We present the experimental results with a dataset composed of real users, comparing the performance of different modules of our algorithm.