Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing content presentation. In one aspect, a system includes a training database that stores training data including attribute information about users and corresponding proxy metrics quantifying behavior by the users following content presentation; a content database; a model generator that accesses the training data and trains a model for content distribution; and a content distribution server that receives a content request, uses the model to select content, transmits data identifying the selected content, wherein the model: obtains a set of attributes for a user associated with the request, receives information about a given content, predicts a proxy metric based on the set of attributes and the information about the content, the predicted proxy metric providing information about subject retention or awareness; and identifies the given content for distribution if the predicted proxy metrics meet a threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a training database that stores training data including attribute information about a plurality of users and corresponding proxy metrics that quantify online behavior by the plurality of users following content presentation; a content database that stores third party content received from various third party content providers; a model generator that accesses the training data in the training database and trains a model for third party content distribution based on the training data; and a content distribution server that receives a request for third party content to be presented with another portion of content at a client device, wherein the content distribution server uses the model to select third party content to be distributed to the client device in response to the request and transmits data identifying the selected third party content to the client device, wherein the model: obtains a set of attributes from the request, wherein the set of attributes relate to information about a user associated with the request, receives information about a given third party content stored in the content database, predicts, for the given third party content, a proxy metric based on the set of attributes and the information about the third party content, wherein the predicted proxy metric provides information about at least one of subject retention or subject awareness that will result from presentation of the given third party content; and identifies, using the predicted proxy metrics and for the content distribution server, the given third party content for distribution based on the predicted proxy metrics meeting a threshold. 2. The system of claim 1, wherein the information about the given third party content in the content database includes at least one of a category of the given third party content, a duration for the given third party content, and a feature of the given third party content. 3. The system of claim 1, wherein the training database further receives proxy metrics associated with displaying particular third party content to the client device, and wherein the model generator further retrains the model using the received proxy metrics associated with displaying the particular third party content to the client device. 4. The system of claim 1, wherein the model further weighs certain categories of attribute information and predicts the proxy metric based on weighted attribute information. 5. The system of claim 1, wherein the request for third party content contains no known attribute information, wherein the model identifies training data regardless of attribute information. 6. The system of claim 1, wherein the proxy metrics include at least one of clicks on the video content, watch time associated with video content, skip time associated with video content, viewability metrics associated with the video content, and audioability associated with the video content. 7. The system of claim 1, wherein each third party content of the third party content received from the various third party content providers includes a preference for subject retention or subject awareness. 8. The system of claim 1, wherein the model identifies the given third party content based on the preference. 9. The system of claim 1, wherein the model generator identifies and stores a correlation between a particular proxy metric and subject retention. 10. The system of claim 1, wherein the model generator identifies and stores a correlation between a particular proxy metric and subject awareness. 11. The system of claim 1, wherein the model predicts the proxy metrics using at least one of logistic regression machine learning and linear regression machine learning. 12. A computer-implemented method comprising: storing, in a training database, training data including attribute information about a plurality of users and corresponding proxy metrics that quantify online behavior following content presentation; storing, in a content database, third party content received from various third party content providers; accessing, by a model generator, the training data in the training database and training a model for third party content distribution based on the training database; receiving, by a content distribution server, a request for third party content to be presented with another portion of content at a client device; selecting, by the content distribution server and using the model, third party content to be distributed to the client device in response to the request, wherein the model: obtains a set of attributes from the request, wherein the set of attributes relate to information about a user associated with the request, receives information about a given third party content stored in the content database, predicts, for the given third party content, a proxy metric based on the set of attributes and the information about the third party content, wherein the predicted proxy metric provides information about at least one of subject retention or subject awareness that will result from presentation of the given third party content; and identifies, using the predicted proxy metrics and for the content distribution server, the given third party content for distribution based on the predicted proxy metrics meeting a threshold; and transmitting, by the content distribution server, data identifying the selected third party content to the client device. 13. The computer-implemented method of claim 12, wherein the information about the given third party content in the content database includes at least one of a category of the given third party content, a duration for the given third party content, and a feature of the given third party content. 14. The computer-implemented method of claim 12, further comprising: receiving, by the training database, proxy metrics associated with displaying particular third party content to the client device; and retraining the model using the received proxy metrics associated with displaying the particular third party content to the client device. 15. The computer-implemented method of claim 12, wherein the model further weighs certain categories of attribute information and predicts the proxy metric based on weighted attribute information. 16. The computer-implemented method of claim 12, wherein the proxy metrics include at least one of clicks on the video content, watch time associated with video content, skip time associated with video content, viewability metrics associated with the video content, and audioability associated with the video content. 17. The computer-implemented method of claim 12, wherein each third party content of the third party content received from the various third party content providers includes a preference for subject retention or subject awareness. 18. The computer-implemented method of claim 12, further comprising: identifying and storing a correlation between a particular proxy metric and subject retention. 19. The computer-implemented method of claim 12, further comprising: identifying and storing a correlation between a particular proxy metric and subject awareness. 20. One or more non-transitory computer-readable media having instructions stored thereon that, when executed by one or more processors, cause performance of operations comprising: storing, in a training database, training data including attribute information about a plurality of users and corresponding proxy metrics that quantify online behavior following content presentation; storing, in a content database, third party content received from various third party content providers; accessing the training data in the training database and training a model for third party content distribution based on the training database; receiving a request for third party content to be presented with another portion of content at a client device; selecting, using the model, third party content to be distributed to the client device in response to the request, wherein the model: obtains a set of attributes from the request, wherein the set of attributes relate to information about a user associated with the request, receives information about a given third party content stored in the content database, predicts, for the given third party content, a proxy metric based on the set of attributes and the information about the third party content, wherein the predicted proxy metric provides information about at least one of subject retention or subject awareness that will result from presentation of the given third party content; and identifies, using the predicted proxy metrics and for the content distribution server, the given third party content for distribution based on the predicted proxy metrics meeting a threshold; and transmitting data identifying the selected third party content to the client device.