Recommendations in a computing advice facility
First Claim
1. A method comprising:
- generating a ratings matrix including matrix values, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items, wherein the ratings matrix includes a missing entry representing an unknown affinity rating;
generating, using one or more processors, a revised ratings matrix by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry;
estimating a confidence value associated with at least a portion of the known affinity ratings in the ratings matrix;
generating a confidence matrix that includes the confidence values;
estimating a probability that a specific user had an opportunity to rate a specific item, the specific user and the specific item being associated with a missing affinity rating in the ratings matrix;
replacing the missing affinity rating in the ratings matrix with a low affinity rating; and
inserting a confidence value associated with the specific user and the specific item in the confidence matrix, the confidence value having a value equal to the probability.
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Accused Products
Abstract
According to various embodiments, a ratings matrix including matrix values is generated, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items. The ratings matrix may include a missing entry representing an unknown affinity rating. According to various embodiments, a revised ratings matrix is generated by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry.
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Citations
18 Claims
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1. A method comprising:
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generating a ratings matrix including matrix values, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items, wherein the ratings matrix includes a missing entry representing an unknown affinity rating; generating, using one or more processors, a revised ratings matrix by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry; estimating a confidence value associated with at least a portion of the known affinity ratings in the ratings matrix; generating a confidence matrix that includes the confidence values; estimating a probability that a specific user had an opportunity to rate a specific item, the specific user and the specific item being associated with a missing affinity rating in the ratings matrix; replacing the missing affinity rating in the ratings matrix with a low affinity rating; and inserting a confidence value associated with the specific user and the specific item in the confidence matrix, the confidence value having a value equal to the probability. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A non-transitory machine-readable storage medium having embodied thereon instructions executable by one or more machines to perform operations comprising:
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generating a ratings matrix including matrix values, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items, wherein the ratings matrix includes a missing entry representing an unknown affinity rating; generating, using one or more processors, a revised ratings matrix by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry; estimating a confidence value associated with at least a portion of the known affinity ratings in the ratings matrix; generating a confidence matrix that includes the confidence values; estimating a probability that a specific user had an opportunity to rate a specific item, the specific user and the specific item being associated with a missing affinity rating in the ratings matrix; replacing the missing affinity rating in the ratings matrix with a low affinity rating; and inserting a confidence value associated with the specific user and the specific item in the confidence matrix, the confidence value having a value equal to the probability.
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16. An apparatus comprising:
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a matrix generation module configured to generate a ratings matrix including matrix values, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items, wherein the ratings matrix includes a missing entry representing an unknown affinity rating; and a prediction module, implemented by one or more processors, configured to; generate a revised ratings matrix by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry; estimate a confidence value associated with at least a portion of the known affinity ratings in the ratings matrix; generate a confidence matrix that includes the confidence values; estimate a probability that a specific user had an opportunity to rate a specific item, the specific user and the specific item being associated with a missing affinity rating in the ratings matrix; replace the missing affinity rating in the ratings matrix with a low affinity rating; and insert a confidence value associated with the specific user and the specific item in the confidence matrix, the confidence value having a value equal to the probability. - View Dependent Claims (17, 18)
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Specification