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.

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.