Collaborative filtering model having improved predictive performance
First Claim
Patent Images
1. A method comprising:
- for each first entity of a subset of a plurality of first entities, estimating by a computing device an expected improvement of a predictive performance of a collaborative filtering model constructed from training data including a plurality of ratings of the first entity in relation to the second entities and that has a predictive accuracy resulting from being tested against testing data, if additional ratings of the first entity in relation to a plurality of second entities were obtained, by;
removing a number of the ratings of the first entity in relation to the second entities from the training data;
constructing a reduced collaborative filtering model from the training data from which the number of the ratings have been removed;
testing the reduced collaborative filtering model against the testing data to determine a predictive accuracy thereof;
determining the expected improvement based on a degradation of the predictive accuracy of the reduced collaborative filtering model compared to the predictive accuracy of the collaborating filtering model;
selecting by a computing device particular first entities from the subset of the first entities of which to obtain the additional ratings in relation to the second entities, based at least on the expected improvements that have been determined; and
obtaining a number of the additional ratings of the particular first entities in relation to the second entities based on the number of the ratings that were removed from the training data.
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Abstract
For each first entity of a subset of a number of first entities, an expected improvement of a predictive performance of a collaborative filtering model if additional ratings of the first entity in relation to a plurality of second entities were obtained is estimated. Particular first entities from the subset of the first entities of which to obtain the additional ratings in relation to the second entities are selected based at least on the expected improvements that have been determined. The additional ratings of the particular first entities in relation to the second entities are obtained.
12 Citations
15 Claims
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1. A method comprising:
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for each first entity of a subset of a plurality of first entities, estimating by a computing device an expected improvement of a predictive performance of a collaborative filtering model constructed from training data including a plurality of ratings of the first entity in relation to the second entities and that has a predictive accuracy resulting from being tested against testing data, if additional ratings of the first entity in relation to a plurality of second entities were obtained, by; removing a number of the ratings of the first entity in relation to the second entities from the training data; constructing a reduced collaborative filtering model from the training data from which the number of the ratings have been removed; testing the reduced collaborative filtering model against the testing data to determine a predictive accuracy thereof; determining the expected improvement based on a degradation of the predictive accuracy of the reduced collaborative filtering model compared to the predictive accuracy of the collaborating filtering model; selecting by a computing device particular first entities from the subset of the first entities of which to obtain the additional ratings in relation to the second entities, based at least on the expected improvements that have been determined; and obtaining a number of the additional ratings of the particular first entities in relation to the second entities based on the number of the ratings that were removed from the training data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A system comprising:
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a processor; a computer-readable data storage medium to store one or more computer programs that are executable by the processor; a first component implemented by the computer programs to, for each first entity of a subset of a plurality of first entities, estimate an expected improvement of a predictive performance of a collaborative filtering model constructed from training data including a plurality of ratings of the first entity in relation to the second entities and that has a predictive accuracy resulting from being tested against testing data, if additional ratings of the first entity in relation to a plurality of second entities were obtained, by; removing a number of the ratings of the first entity in relation to the second entities from the training data; constructing a reduced collaborative filtering model from the training data from which the number of the ratings of the first entity in relation to the second entities have been removed; testing the reduced collaborative filtering model against the testing data to determine a predictive accuracy of the reduced collaborative filtering model; determining the expected improvement based on a degradation of the predictive accuracy of the reduced collaborative filtering model compared to the predictive accuracy of the collaborative filtering model; a second component implemented by the computer programs to select particular first entities from the subset of the first entities of which to obtain the additional ratings in relation to the second entities, based at least on the expected improvements that have been determined; and
,a third component implemented by the computer programs to obtain a number of the additional ratings of the particular first entities in relation to the second entities based on the number of the ratings that were removed from the training data.
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15. A non-transitory computer-readable data storage medium having one or more computer programs stored thereon, wherein execution of the computer programs by a processor causes a method to be performed, the method comprising:
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for each first entity of a subset of a plurality of first entities, estimating by a computing device an expected improvement of a predictive performance of a collaborative filtering model constructed from training data including a plurality of ratings of the first entity in relation to the second entities and that has a predictive accuracy resulting from being tested against testing data, if additional ratings of the first entity in relation to a plurality of second entities were obtained, by; removing a number of the ratings of the first entity in relation to the second entities from the training data; constructing a reduced collaborative filtering model from the training data from which the number of the ratings have been removed; testing the reduced collaborative filtering model against the testing data to determine a predictive accuracy thereof; determining the expected improvement based on a degradation of the predictive accuracy of the reduced collaborative filtering model compared to the predictive accuracy of the collaborating filtering model; selecting by a computing device particular first entities from the subset of the first entities of which to obtain the additional ratings in relation to the second entities, based at least on the expected improvements that have been determined; and obtaining a number of the additional ratings of the particular first entities in relation to the second entities based on the number of the ratings that were removed from the training data.
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Specification