Using Polling Results as Discrete Metrics For Content Quality Prediction Model
First Claim
1. A computer-implemented method comprising:
- determining interests for each of a plurality of users of a social networking system, a respective interest for each user based on information provided by the respective user;
inferring a preference of each user for a first content item over a second content item in a pair-wise comparison of the first and second content items, the inferring based on at least one of the determined interests for the respective user;
identifying at least one predictive factor associated with each inferred preference;
for each identified predictive factor, determining a feedback coefficient for the identified predictive factor based on at least a subset of the inferred preferences; and
storing the feedback coefficients for the identified predictive factors in a computer-readable storage medium.
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Abstract
A social networking system presents content items to users, who then provide feedback regarding pairs of content items. The feedback includes a selection of a content item of the pair of content items that was preferred by the user over the other content item. The social networking system uses this information to train a predictive model that scores content items based on quality. The content items may be advertisements. The social networking system uses the pair-wise comparisons of the advertisements to determine feedback coefficients in an advertising quality score prediction model using regression analysis of the pair-wise comparisons for each predictive factor in the model. In this way, the pair-wise comparisons are used to train the prediction model to understand which advertisements are more enjoyable than others. A feedback coefficient for each predictive factor may be computed based on the preferences received from the group of users.
20 Citations
23 Claims
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1. A computer-implemented method comprising:
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determining interests for each of a plurality of users of a social networking system, a respective interest for each user based on information provided by the respective user; inferring a preference of each user for a first content item over a second content item in a pair-wise comparison of the first and second content items, the inferring based on at least one of the determined interests for the respective user; identifying at least one predictive factor associated with each inferred preference; for each identified predictive factor, determining a feedback coefficient for the identified predictive factor based on at least a subset of the inferred preferences; and storing the feedback coefficients for the identified predictive factors in a computer-readable storage medium. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A non-transitory computer-readable storage medium storing instructions, the instructions when executed by a processor in a social networking system for predicting quality of content items, causes the processor to:
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determine interests for each of a plurality of users of a social networking system, a respective interest for each user based on information provided by the respective user; infer a preference of each user for a first content item over a second content item in a pair-wise comparison of the first and second content items, the inferring based on at least one of the determined interests for the respective user; identify at least one predictive factor associated with each inferred preference; for each identified predictive factor, determine a feedback coefficient for the identified predictive factor based on at least a subset of the inferred preferences; and store the feedback coefficients for the identified predictive factors in a computer-readable storage medium. - View Dependent Claims (13, 14, 15, 16, 17)
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18. A system comprising:
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a processor; a non-transitory computer readable storage medium for storing software modules; and a polling analysis module configured to; determine interests for each of a plurality of users of a social networking system, a respective interest for each user based on information provided by the respective user; infer a preference of each user for a first content item over a second content item in a pair-wise comparison of the first and second content items, the inferring based on at least one of the determined interests for the respective user; identify at least one predictive factor associated with each inferred preference; for each identified predictive factor, determine a feedback coefficient for the identified predictive factor based on at least a subset of the inferred preferences; and store the feedback coefficients for the identified predictive factors in a computer-readable storage medium. - View Dependent Claims (19, 20, 21, 22, 23)
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Specification