Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: determining, by a computer, a plurality of demographic groups of users of a social networking system, the determining comprising, for each demographic group of users, selecting the users based on demographic characteristics of the users; for each demographic group of users: selecting a set of features for use for the demographic group based on the demographic characteristics of the users of the demographic group; training a model configured to rank news feed stories for presentation to users of the demographic group, the training utilizing training sets obtained from the demographic group of users, the model comprising the set of features associated with the demographic group; identifying stories for presentation to a user belonging to a demographic group; and ranking the stories identified for presentation to the user using the model corresponding to the demographic group of the user and presenting the stories to the user based on the ranking. 2. The method of claim 1, wherein a first model for a first demographic group of users is trained using a first set of features and a second model for a second demographic group of users is trained using a second set of features. 3. The method of claim 1, wherein selecting the users for a demographic group is based on demographic characteristics of other users connected to the user in the social networking system. 4. The method of claim 1, further comprising: for each demographic group of users, periodically retraining the model for the demographic group of users. 5. The method of claim 4, further comprising: determining a rate at which the model for a demographic group is periodically retrained based on interactions of users of the demographic group with the news feed stories. 6. The method of claim 4, further comprising: determining a rate at which the model for a demographic group is periodically retrained based on interactions of users of the demographic group with objects associated with newsfeed stories, the objects represented in the social networking system. 7. The method of claim 1, wherein the demographic characteristics for determining a demographic group include one or more of: age of users, ethnicity of users, gender of users, and income of users. 8. The method of claim 1, wherein the features used for the model comprise interactions of users of the demographic group with news feed stories presented to the users of the demographic group. 9. The method of claim 8, wherein an interaction with a news feed story comprises one of selecting a link in the news feed story, commenting on the news feed story, liking the news feed story, or hiding the news feed story. 10. The method of claim 1, wherein the features used for the model comprise interactions of users of the demographic group with objects associated with news feed stories presented to the users of the demographic group. 11. The method of claim 10, wherein an object associated with a news feed story comprises one of an image, a video, a comment posted by a user, or a user profile. 12. A computer-implemented method comprising: determining, by a computer, a plurality of demographic groups of users of a social networking system, the determining comprising, for each demographic group of users, selecting the users based on demographic characteristics of the users; for each demographic group of users: selecting a set of features for use for the demographic group based on the demographic characteristics of the users of the demographic group, wherein features of the set comprise interactions of users of the demographic group with news feed stories presented to the users; training a model configured to rank news feed stories for presentation to users of the demographic group, the training utilizing training sets obtained from the demographic group of users, the model comprising the set of features associated with the demographic group; and storing information describing the model. 13. The method of claim 12, wherein a first model for a first demographic group of users is trained using a first set of features and a second model for a second demographic group of users is trained using a second set of features. 14. The method of claim 12, wherein a user is selected for a demographic group based on demographic characteristics of other users connected to the user in the social networking system. 15. The method of claim 12, further comprising: for each demographic group of users, periodically retraining the model at a rate determined using the interactions of users of the demographic group with the news feed stories. 16. The method of claim 12, wherein the demographic characteristics for determining a demographic group include one or more of: age of users, ethnicity of users, gender of users, and income of users. 17. A non-transitory computer-readable storage medium storing computer-executable code for ranking news feed stories of a social networking system, the code comprising instructions for: determining, by a computer, a plurality of demographic groups of users of a social networking system, the determining comprising, for each demographic group of users, selecting the users based on demographic characteristics of the users; for each demographic group of users: selecting a set of features for use for the demographic group based on the demographic characteristics of the users of the demographic group; training a model configured to rank news feed stories for presentation to users of the demographic group, the training utilizing training sets obtained from the demographic group of users, the model comprising the set of features associated with the demographic group; identifying stories for presentation to a user belonging to a demographic group; and ranking the stories identified for presentation to the user using the model corresponding to the demographic group of the user and presenting the stories to the user based on the ranking. 18. The non-transitory computer-readable storage medium of claim 17, wherein a first model corresponding to a first demographic group of users is trained using a first set of features and a second model based on a second demographic group of users is trained using a second set of features. 19. The non-transitory computer-readable storage medium of claim 17, wherein the code further comprises instructions for: for each demographic group of users, periodically retraining the model at a rate determined using interactions of users of the demographic group with the news feed stories. 20. The non-transitory computer-readable storage medium of claim 17, wherein the demographic characteristics for determining a demographic group include one or more of: age of users, ethnicity of users, gender of users, and income of users.