1. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: receive user information about a target user of an online system, the user information describing interactions performed by the target user on the online system; receive 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; train a machine learning model using feature vectors identified based on actions performed by a plurality of users of the online system and based on the information about the plurality of users, the actions describing items that at least one of the plurality of users previously acquired, the trained machine learning model configured to determine a likelihood that a particular user will acquire a particular item; receive item information indicating a change in a status of the item associated with the third party; provide as inputs to the machine learning model, the user information, the third party information, and the item information; determine, by the trained machine learning model based on the inputs, a likelihood that the target user will acquire the item based on the change in status of the item; send a content item associated with the item for display on a client device of the target user in response to the likelihood that the target user will acquire the item exceeding a threshold value.
6. The non-transitory computer readable storage medium of claim 1 , wherein the user information further describes interactions performed by other users of the online system connected to the target user, at least one of the interactions describing another item previously acquired by one of the other users, the another item matching the item based on one or more characteristics.