13. 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; training 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; receiving item information indicating a change in a status of the item associated with the third party; providing as inputs to the trained machine learning model, the user information, the third party information, and the item information; determining, 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; sending 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.
19. The method of claim 13 , wherein the feature vectors are each associated with a weight, a first vector of the feature vectors identified based on actions performed by users connected to the target user on the online system, a second vector of the feature vectors identified based on actions performed by users not connected to the target user on the online system, the first vector weighted higher than the second vector.