Predicting content performance with interest data
First Claim
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1. A method of selecting content, comprising:
- receiving, at a processing circuit, a content selection request for third-party content to be presented by a client device in conjunction with first-party content, the content selection request including a client device identifier;
retrieving data indicative of a set of one or more topical interest categories associated with the client device identifier;
identifying, by the processing circuit, input values for a prediction model, the input values including;
the set of topical interest categories,a click propensity metric associated with the client identifier, the click propensity metric indicative of how often the client device submitted clicks on previously selected third-party content, anda length of time each topical interest category has been associated with the client device identifier, wherein a length of time greater than a defined time period indicates a long-term topical interest category and a length of time less than the defined time period indicates a short-term topical interest category;
performing, by the processing circuit, one or more of image recognition or text recognition on each of a set of third-party content to determine a topical interest category;
selecting, by the processing circuit, a subset of third-party content eligible for selection based in part on the set of topical interest categories associated with the client device identifier, a topical interest category of each of the subset of third-party content, and a topic of first party content;
using the prediction model that includes a logistic regression model to predict, based on the input values including the length of time, a predicted action metric for the third-party content in the subset, the predicted action metric corresponding to a predicted likelihood of an online action occurring as a result of the third-party content being selected, the logistic regression model indicating the predicted likelihood of the online action as
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Abstract
Systems and methods for predicting content performance with interest data include receiving a content selection request that includes a client identifier. One or more topical interest categories associated with the client identifier may be used as inputs to a prediction model to predict the likelihood of an online action occurring as a result of third-party content being selected. The predicted likelihood may be used to select third-party content.
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Citations
17 Claims
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1. A method of selecting content, comprising:
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receiving, at a processing circuit, a content selection request for third-party content to be presented by a client device in conjunction with first-party content, the content selection request including a client device identifier; retrieving data indicative of a set of one or more topical interest categories associated with the client device identifier; identifying, by the processing circuit, input values for a prediction model, the input values including; the set of topical interest categories, a click propensity metric associated with the client identifier, the click propensity metric indicative of how often the client device submitted clicks on previously selected third-party content, and a length of time each topical interest category has been associated with the client device identifier, wherein a length of time greater than a defined time period indicates a long-term topical interest category and a length of time less than the defined time period indicates a short-term topical interest category; performing, by the processing circuit, one or more of image recognition or text recognition on each of a set of third-party content to determine a topical interest category; selecting, by the processing circuit, a subset of third-party content eligible for selection based in part on the set of topical interest categories associated with the client device identifier, a topical interest category of each of the subset of third-party content, and a topic of first party content; using the prediction model that includes a logistic regression model to predict, based on the input values including the length of time, a predicted action metric for the third-party content in the subset, the predicted action metric corresponding to a predicted likelihood of an online action occurring as a result of the third-party content being selected, the logistic regression model indicating the predicted likelihood of the online action as - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for selecting content, comprising a processing circuit operable to:
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receive a content selection request for third-party content to be presented by a client device in conjunction with first-party content, the content selection request including a client device identifier; retrieve data indicative of a set of one or more topical interest categories associated with the client device identifier; identify input values for a prediction model, the input values including; the set of topical interest categories, a click propensity metric associated with the client identifier, the click propensity metric indicative of how often the client device submitted clicks on previously selected third-party content, and a length of time each topical interest category has been associated with the client device identifier, wherein a length of time greater than a defined time period indicates a long-term topical interest category and a length of time less than the defined time period indicates a short-term topical interest category; perform one or more of image recognition or text recognition on each of a set of third-party content to determine a topical interest category; select a subset of third-party content eligible for selection based in part on the set of topical interest categories associated with the client device identifier, a topical interest category of each of the subset of third-party content, and a topic of first party content; use the prediction model that includes a logistic regression model to predict, based on the input values including the length of time, a predicted action metric for the third-party content in the subset, the predicted action metric corresponding to a predicted likelihood of an online action occurring as a result of the third-party content being selected, the logistic regression model indicating the predicted likelihood of the online action as - View Dependent Claims (9, 10, 11, 12, 13)
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14. A non-transitory computer-readable storage medium having machine instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to perform operations comprising:
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receiving a content selection request for third-party content to be presented by a client device in conjunction with first-party content, the content selection request including a client device identifier; retrieving data indicative of a set of one or more topical interest categories associated with the client device identifier; identifying input values for a prediction model, the input values including; the set of topical interest categories, a click propensity metric associated with the client identifier, the click propensity metric being indicative of how often the client device submitted clicks on previously selected third-party content, and a length of time each topical interest category has been associated with the client device identifier, wherein a length of time greater than a defined time period indicates a long-term topical interest category and a length of time less than the defined time period indicates a short-term topical interest category; performing, by the processing circuit, one or more of image recognition or text recognition on each of a set of third-party content to determine a topical interest category; selecting a subset of third-party content eligible for selection based in part on the set of topical interest categories associated with the client device identifier, a topical interest category of each of the subset of third-party content, and a topic of first party content; using the prediction model that includes a logistic regression model to predict, based on the input values including the length of time, a predicted action metric for the third-party content in the subset, the predicted action metric corresponding to a predicted likelihood of an online action occurring as a result of the third-party content being selected, the logistic regression model indicating the predicted likelihood of the online action as - View Dependent Claims (15, 16, 17)
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Specification