PROMPT RANKING
First Claim
1. A method comprising:
- by a computing device and for a user of a social-networking system, accessing a plurality of candidate notifications stored on the social-networking system, wherein each of the candidate notifications comprises a prompt to perform an action on the social-networking system;
by the computing device, generating a user feature vector quantifying features of the user;
by the computing device, assessing, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications, wherein the one or more feature vectors comprises the user feature vector;
by the computing device, ranking each of the candidate notifications based at least in part on the respective calculated interaction score; and
by the computing device, providing, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user, wherein each of the provided candidate notifications satisfies a pre-determined threshold value.
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Accused Products
Abstract
In one embodiment, a method includes accessing a number of candidate notifications stored on the social-networking system. Each of the candidate notifications includes a prompt to perform an action on the social-networking system. The method also includes generating a user feature vector quantifying features of the user; and assessing, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications. The one or more feature vectors includes the user feature vector. The method also includes ranking each of the candidate notifications based at least in part on the respective calculated interaction score; and providing, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user. Each of the provided candidate notifications satisfies a pre-determined threshold value.
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Citations
20 Claims
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1. A method comprising:
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by a computing device and for a user of a social-networking system, accessing a plurality of candidate notifications stored on the social-networking system, wherein each of the candidate notifications comprises a prompt to perform an action on the social-networking system; by the computing device, generating a user feature vector quantifying features of the user; by the computing device, assessing, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications, wherein the one or more feature vectors comprises the user feature vector; by the computing device, ranking each of the candidate notifications based at least in part on the respective calculated interaction score; and by the computing device, providing, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user, wherein each of the provided candidate notifications satisfies a pre-determined threshold value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. One or more computer-readable non-transitory storage media embodying software configured when executed to:
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for a user of a social-networking system, access a plurality of candidate notifications stored on the social-networking system, wherein each of the candidate notifications comprises a prompt to perform an action on the social-networking system; generate a user feature vector quantifying features of the user; assess, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications, wherein the one or more feature vectors comprises the user feature vector; rank each of the candidate notifications based at least in part on the respective calculated interaction score; and provide, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user, wherein each of the provided candidate notifications satisfies a pre-determined threshold value. - View Dependent Claims (16, 17)
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18. A device comprising:
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one or more processors; and one or more computer-readable non-transitory storage media coupled to the processors and embodying software configured when executed to; for a user of a social-networking system, access a plurality of candidate notifications stored on the social-networking system, wherein each of the candidate notifications comprises a prompt to perform an action on the social-networking system; generate a user feature vector quantifying features of the user; assess, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications, wherein the one or more feature vectors comprises the user feature vector; rank each of the candidate notifications based at least in part on the respective calculated interaction scores; and provide, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user, wherein each of the provided candidate notifications satisfies a pre-determined threshold value. - View Dependent Claims (19, 20)
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Specification