9. A method comprising: receiving user information about a target user of an online system, the user information describing interactions performed by the target user on the online system; receiving third party information from a third party outside of the online system, the third party information describing an item associated with the third party that the target user is interested in acquiring from the third party; retrieving a plurality of trained machine learning models each associated with a population of users of the online system, each trained machine learning model configured to determine a likelihood that a user matching the corresponding population of users will perform a transaction associated with a particular item; receiving item information indicating a change in a status of the item; selecting a trained machine learning model of the plurality of trained machine learning models based at least in part on the user information; determining, using the selected trained machine learning model, the user information, the third party information, and the item information, a likelihood that the target user will perform a transaction associated with the item; sending, in response to the likelihood that the target user will perform the transaction associated with the item exceeding a threshold value, a content item associated with the item for display on a client device of the target user.
9. A method comprising: receiving user information about a target user of an online system, the user information describing interactions performed by the target user on the online system; receiving third party information from a third party outside of the online system, the third party information describing an item associated with the third party that the target user is interested in acquiring from the third party; retrieving a plurality of trained machine learning models each associated with a population of users of the online system, each trained machine learning model configured to determine a likelihood that a user matching the corresponding population of users will perform a transaction associated with a particular item; receiving item information indicating a change in a status of the item; selecting a trained machine learning model of the plurality of trained machine learning models based at least in part on the user information; determining, using the selected trained machine learning model, the user information, the third party information, and the item information, a likelihood that the target user will perform a transaction associated with the item; sending, in response to the likelihood that the target user will perform the transaction associated with the item exceeding a threshold value, a content item associated with the item for display on a client device of the target user. 11. The method of claim 9 , wherein a first trained machine learning model of the plurality of trained machine learning models is associated with a first population of users who acquire items using mobile phones or tablets, and wherein a second trained machine learning model of the plurality of trained machine learning models is associated with a second population of users who acquire items using computers or laptops.