An online system selects a number of content items and presents the selected content items through a feed to a target user, where each selected candidate content item is likely to cause the target user to post his/her new content in response to the selected candidate content item within a short period of time. The online system selects the candidate content items for presentation through the feed using a trained post session prediction model. A ranking score for a candidate content item is determined based on a probability value indicating likelihood that the candidate content item causes the target user to post new content. The probability value is determined by applying a trained model to user features of the target user and content features of the candidate content item. The online system ranks the candidate content items based on their ranking scores and present the feed to the target user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer method, comprising: receiving, by an online system, a plurality of candidate content items to be included in a news feed for a target user of the online system, each candidate content item having a plurality of content features, and the target user interacting with a content item by performing one or more actions on the candidate content item; obtaining a plurality of user features describing the target user; for each candidate content item: obtaining the plurality of content features associated with each candidate content item; applying a trained model to the plurality of user features and the plurality of content features to determine a probability value indicating a likelihood that the target user posts new content if the candidate content item is presented to the target user; and generating a ranking score for the candidate content item based on the probability value; ranking the candidate content items based on their corresponding ranking scores; selecting a number of candidate content items to be included in the news feed for the target user; and presenting the news feed including the selected number of candidate content items to the target user. 2. The method of claim 1, further comprising training a model to determine the probability value indicating the likelihood that the target user posts new content in response to a candidate content item, wherein the training comprises: obtaining training data from user features of a corpus of users of the online system and from content features of a corpus of content items of the online system; obtaining interactions by the users with the content items, each interaction having timing information and type of interaction, the timing information describing when a user has interacted with a content item, and the type of interaction describing a type of interaction performed by the user on the content item; and for each type of interaction, training a model to predict a probability that a user is likely to perform the type of interaction with the content time within a predefined period of time. 3. The method of claim 2, wherein the user features of the target user are selected from a group consisting of: positing history of the target user, attributes of posts of the target user, and biographic, demographic, or other types of descriptive information of the target user. 4. The method of claim 2, wherein the content features of the candidate content item are selected from a group consisting of: one or more topics, timing information describing a post on the content item, and content types associated with the candidate content item. 5. The method of claim 1, wherein generating a ranking score for the candidate content item based on the probability value comprises multiplying the probability value of the candidate content item with a predetermined weight of posting new content action type. 6. The method of claim 1, wherein generating a ranking score for the candidate content item based on the probability value further comprises: obtaining a posting score by multiplying the probability value of the candidate content item with a predetermined weight assigned to posting new content action type; obtaining an interaction score for each of a plurality of interactions; and aggregating the posting score with the interaction score for each of the plurality of interactions. 7. The method claim 6, where obtaining an interaction score for each of a plurality of interactions comprises: receiving a weight assigned to the type of the interaction; and determining a probability value that the target user interacts with the candidate content item in the specific type of interaction. 8. The method of claim 6, where aggregating the posting score with the interaction score for each of the plurality of interactions comprises: adding the posting score of a candidate content item with the interaction score for each of the plurality of interactions associated with the candidate content item to generate a total score; and generating a ranking score for the candidate content item based on the generated total score of the candidate content item. 9. The method of claim 6, wherein a type of interaction is selected from a group consisting of: expressing a preference for the candidate content item, providing a comment associated with the candidate content item, sharing the candidate content item with another user, and any combination thereof. 10. The method of claim 1, wherein presenting the news feed to the target user comprises presenting the selected candidate content items in an order determined based on their rankings. 11. A non-transitory computer readable storage medium storing executable computer program instructions, the computer program instructions comprising instructions that when executed cause a computer processor to: receive, by an online system, a plurality of candidate content items to be included in a news feed for a target user of the online system, each candidate content item having a plurality of content features, and the target user interacting with a content item by performing one or more actions on the candidate content item; obtain a plurality of user features describing the target user; for each candidate content item: obtain the plurality of content features associated with each candidate content item; apply a trained model to the plurality of user features and the plurality of content features to determine a probability value indicating a likelihood that the target user posts new content if the candidate content item is presented to the target user; and generate a ranking score for the candidate content item based on the probability value; rank the candidate content items based on their corresponding ranking scores; select a number of candidate content items to be included in the news feed for the target user; and present the news feed including the selected number of candidate content items to the target user. 12. The computer readable medium of claim 11, further comprising instructions that cause the processor to train a model to determine the probability value indicating the likelihood that the target user posts new content in response to a candidate content item, wherein instructions that cause the processor to train the model comprise instructions that cause the processor to: obtain training data from user features of a corpus of users of the online system and from content features of a corpus of content items of the online system; obtain interactions by the users with the content items, each interaction having timing information and type of interaction, the timing information describing when a user has interacted with a content item, and the type of interaction describing a type of interaction performed by the user on the content item; and for each type of interaction, train a model to predict a probability that a user is likely to perform the type of interaction with the content time within a predefined period of time. 13. The computer readable medium of claim 12, wherein the user features of the target user are selected from a group consisting of: positing history of the target user, attributes of posts of the target user, and biographic, demographic, or other types of descriptive information of the target user. 14. The computer readable medium of claim 12, wherein the content features of the candidate content item are selected from a group consisting of: one or more topics, timing information describing a post on the content item, and content types associated with the candidate content item. 15. The computer readable medium of claim 11, wherein the instructions that cause the processor to generate a ranking score for the candidate content item based on the probability value comprise instructions that cause the processor to multiply the probability value of the candidate content item with a predetermined weight of posting new content action type. 16. The computer readable medium of claim 11, wherein the instructions that cause the processor to generate a ranking score for the candidate content item based on the probability value further comprise instructions that cause the processor to: obtain a posting score by multiplying the probability value of the candidate content item with a predetermined weight assigned to posting new content action type; obtain an interaction score for each of a plurality of interactions; and aggregate the posting score with the interaction score for each of the plurality of interactions. 17. The computer readable medium of claim 16, where the instructions that cause the processor to obtain an interaction score for each of a plurality of interactions comprise instructions that cause the processor to: receive a weight assigned to the type of the interaction; and determine a probability value that the target user interacts with the candidate content item in the specific type of interaction. 18. The computer readable medium of claim 16, where the instructions that cause the processor to aggregate the posting score with the interaction score for each of the plurality of interactions comprise instructions that cause the processor to: add the posting score of a candidate content item with the interaction score for each of the plurality of interactions associated with the candidate content item to generate a total score; and generate a ranking score for the candidate content item based on the generated total score of the candidate content item. 19. The computer readable medium of claim 16, wherein a type of interaction is selected from a group consisting of: expressing a preference for the candidate content item, providing a comment associated with the candidate content item, sharing the candidate content item with another user, and any combination thereof. 20. The computer readable medium of claim 11, wherein the instructions that cause the processor to present the news feed to the target user comprise instructions that cause the processor to present the selected candidate content items in an order determined based on their rankings.