Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
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
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Matrix completion from a few entries
IEEE Transactions on Information Theory
Matrix Completion from Noisy Entries
The Journal of Machine Learning Research
Factorization models for context-/time-aware movie recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Recovering Low-Rank Matrices From Few Coefficients in Any Basis
IEEE Transactions on Information Theory
Group recommendation in context
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Time feature selection for identifying active household members
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning Rating Patterns for Top-N Recommendations
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4 536 891 ratings provided by 171 670 users on 23 974 movies, as well as the household groupings of a subset of the users. The test dataset comprises 5 450 ratings for which the user label is missing, but the household label is provided. The challenge required to identify the user labels for the ratings in the test set. Our main finding is that temporal information (time labels of the ratings) is significantly more useful for achieving this objective than the user preferences (the actual ratings). Using a model that leverages on this fact, we are able to identify users within a known household with an accuracy of approximately 96% (i.e. misclassification rate around 4%).