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
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
SIAM Journal on Matrix Analysis and Applications
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Successively alternate least square for low-rank matrix factorization with bounded missing data
Computer Vision and Image Understanding
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
The Journal of Machine Learning Research
Fast als-based matrix factorization for explicit and implicit feedback datasets
Proceedings of the fourth ACM conference on Recommender systems
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Low-rank matrix factorization with missing data has become an effective methodology for collaborative filtering applications since it can generate high quality rating predictions for recommendation systems. The performance of low-rank factorization, however, critically depends on how the low-rank model is regularized in order to mitigate the over-fitting problem to the observed data. The objective of this paper is to propose a novel regularization technique which we call inducible regularization. It utilizes pre-estimated ratings on a pre-specified subset of the ratings to regularize the solutions of low-rank matrix factorization. We develop two algorithms for solving the new regularized problem via alternating least squares iterations and stochastic gradient descent. We also devise a fast implementation of the alternating least squares algorithm which is suitable for parallel computing. Numerical experiments on three real-world data sets MovieLens, Jester, and EachMovie are given for comparing the proposed algorithms with existing algorithms ALS, SGD, and SVD++ that solve low-rank matrix factorization with classical regularizations, illustrating superior performance of our proposed algorithms.