ROBUST BAYESIAN MATRIX FACTORIZATION AND RECOMMENDER SYSTEMS USING SAME
First Claim
1. An apparatus comprising:
- an electronic data processing device configured to perform a recommender method including;
performing Bayesian Matrix Factorization (BMF) on a matrix having user and item dimensions using non-Gaussian priors to train a probabilistic collaborative littering model; and
generating a recommendation for a user comprising a predicted item rating or identification of one or more recommended items using the probabilistic collaborative flittering model.
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Abstract
In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column.
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Citations
20 Claims
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1. An apparatus comprising:
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an electronic data processing device configured to perform a recommender method including; performing Bayesian Matrix Factorization (BMF) on a matrix having user and item dimensions using non-Gaussian priors to train a probabilistic collaborative littering model; and generating a recommendation for a user comprising a predicted item rating or identification of one or more recommended items using the probabilistic collaborative flittering model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A storage medium storing instructions executable by an electronic data processing device to perform a recommender method including:
- (i) performing Bayesian Matrix Factorization (BMF) on a matrix having user and item dimensions using priors having noise terms that vary with both row and column position in the matrix to train a probabilistic collaborative filtering model and (ii) generating a recommendation for a user comprising a predicted item rating or identification of one or more recommended items using the probabilistic collaborative filtering model.
- View Dependent Claims (10, 11)
- 12. The storage medium of claim 12, wherein the non-Gaussian priors comprise Gaussian scale mixture priors.
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13. The storage medium of claim 13, wherein the hetersedastic noise terms comprise variance values of the Gaussian scale mixture priors that include (1) a row-dependent variance component that depends upon the matrix row and (2) a column-dependent variance component that depends upon the matrix column.
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15. A method comprising:
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performing Bayesian Matrix Factorization (BMF) on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users wherein the BMF employs priors that include (1) a row-dependent variance component that depends upon the matrix row and (2) a column-dependent variance component that depends upon the matrix column to train probabilistic collaborative filtering model; and generating a recommendation for a user comprising a predicted item rating or identification of one or more recommended items using the probabilistic collaborative filtering model; wherein the performing and the generating are performed by an electronic data processing device.
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- 16. The method of claim 16, wherein the priors comprise non-Gaussian priors.
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17. The method of claim 17, wherein the non-Gaussian priors comprise Gaussian scale mixture priors.
Specification