Recommender system with training function based on non-random missing data
First Claim
1. A method for use in a recommender system, the method comprising:
- obtaining observed feedback data;
constructing a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data;
optimizing one or more parameters of the model using a training objective function; and
generating a list of recommended items for a given user based on the optimized model;
wherein the constructing step comprises;
determining whether each of a plurality of item-user pairs are associated with at least one of the observed feedback data and the additional feedback data that is missing from the observed feedback data; and
responsive to said determining, assigning the plurality of item-user pairs to respective ones of a plurality of different classes;
wherein the training objective function utilizes weights associated with respective ones of the item-user pairs, the weights assigned to the item-user pairs being based at least in part on the classes of the item-user pairs;
wherein the training objective function comprises at least one of;
a penalized log likelihood logistic regression using the weights for item-user pairs, the weights being binary weights;
a least squares regression using the weights for item-user pairs, the weights being binary weights;
a regression using the weights for item-user pairs, the regression being based at least in part on a comparison of observed or imputed ratings to predicted ratings for one or more item-user pairs; and
a regression using the weights for item-user pairs, the regression being based at least in part on a given item-user pair having two or more observed or imputed ratings; and
wherein the obtaining, constructing, optimizing and generating steps are implemented in a processing device comprising a processor coupled to a memory.
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Abstract
A processing device of an information processing system is operative to obtain observed feedback data, to construct a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data, to optimize one or more parameters of the model using a training objective function, and to generate a list of recommended items for a given user based on the optimized model. In illustrative embodiments, the missing feedback data comprises data that is missing not at random (MNAR), and the model comprises a matrix factorization model. The processing device may implement a recommender system comprising a training module coupled to a recommendation module.
6 Citations
24 Claims
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1. A method for use in a recommender system, the method comprising:
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obtaining observed feedback data; constructing a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data; optimizing one or more parameters of the model using a training objective function; and generating a list of recommended items for a given user based on the optimized model; wherein the constructing step comprises; determining whether each of a plurality of item-user pairs are associated with at least one of the observed feedback data and the additional feedback data that is missing from the observed feedback data; and responsive to said determining, assigning the plurality of item-user pairs to respective ones of a plurality of different classes; wherein the training objective function utilizes weights associated with respective ones of the item-user pairs, the weights assigned to the item-user pairs being based at least in part on the classes of the item-user pairs; wherein the training objective function comprises at least one of; a penalized log likelihood logistic regression using the weights for item-user pairs, the weights being binary weights; a least squares regression using the weights for item-user pairs, the weights being binary weights; a regression using the weights for item-user pairs, the regression being based at least in part on a comparison of observed or imputed ratings to predicted ratings for one or more item-user pairs; and a regression using the weights for item-user pairs, the regression being based at least in part on a given item-user pair having two or more observed or imputed ratings; and wherein the obtaining, constructing, optimizing and generating steps are implemented in a processing device comprising a processor coupled to a memory. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 21, 22, 23, 24)
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12. An apparatus comprising:
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a processing device comprising a processor having an associated memory; wherein the processing device is operative; to obtain observed feedback data; to construct a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data; to optimize one or more parameters of the model using a training objective function; and to generate a list of recommended items for a given user based on the optimized model; wherein constructing the model comprises; determining whether each of a plurality of item-user pairs are associated with at least one of the observed feedback data and the additional feedback data that is missing from the observed feedback data; and responsive to said determining, assigning the plurality of item-user pairs to respective ones of a plurality of different classes; and wherein the training objective function utilizes weights associated with respective ones of the item-user pairs, the weights assigned to the item-user pairs being based at least in part on the classes of the item-user pairs; and wherein the training objective function comprises at least one of; a penalized log likelihood logistic regression using the weights for item-user pairs, the weights being binary weights; a least squares regression using the weights for item-user pairs, the weights being binary weights; a regression using the weights for item-user pairs, the regression being based at least in part on a comparison of observed or imputed ratings to predicted ratings for one or more item-user pairs; and a regression using the weights for item-user pairs, the regression being based at least in part on a given item-user pair having two or more observed or imputed ratings. - View Dependent Claims (13, 14, 15, 16)
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17. A recommender system comprising:
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a training module; and a recommendation module coupled to the training module; wherein the training module is configured to apply a training objective function to optimize one or more parameters of a model that accounts for both observed feedback data and additional feedback data that is missing from the observed feedback data; wherein the recommendation module generates a list of recommended items for a given user based on the optimized model; wherein the model is constructed at least in part by; determining whether each of a plurality of item-user pairs are associated with at least one of the observed feedback data and the additional feedback data that is missing from the observed feedback data; and responsive to said determining, assigning the plurality of item-user pairs to respective ones of a plurality of different classes; wherein the training objective function utilizes weights associated with respective ones of the item-user pairs, the weights assigned to the item-user pairs being based at least in part on the classes of the item-user pairs; wherein the training objective function comprises at least one of; a penalized log likelihood logistic regression using the weights for item-user pairs, the weights being binary weights; a least squares regression using the weights for item-user pairs, the weights being binary weights; a regression using the weights for item-user pairs, the regression being based at least in part on a comparison of observed or imputed ratings to predicted ratings for one or more item-user pairs; and a regression using the weights for item-user pairs, the regression being based at least in part on a given item-user pair having two or more observed or imputed ratings; and wherein the recommender system is implemented using at least one processor device. - View Dependent Claims (18, 19, 20)
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Specification