An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory. The probabilistic classifier may include a Naïve-Bayes method. Prediction sets may be generated for each of a plurality of individuals, and used to predict a feature associated with a group. For example, an advertisement may be selected and displayed based on the predicted feature.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A method comprising: identifying a plurality of individual data objects in a data store, each of the plurality of data objects defining features of a respective set of individuals; identifying a data object of the plurality of data objects, the data object identifying a first set of individuals having a first feature; selecting a plurality of content tags based on the first feature; tagging a plurality of content items with one or more of the plurality of content tags; identifying a first content item of the plurality of content items, the first content item being associated with a first content tag, the first content tag being associated with the first set of individuals; selecting a second content item based on the first content tag; generating a display layout for the first and second content items; and transmitting, to a display device, the first and second content item, the display layout, and a display time, wherein, the display device is configured to display the first and second content items using the display layout, the second content item being displayed for the display time. 22. The method of claim 21, wherein the selecting of the plurality of content tags comprises: for the first feature, determining a respective probability distribution indicating, for each of respective content tag among the plurality of content tags, a probability that a set of individuals associated with a content tag has the first feature, thereby generating a plurality of probability distributions; using a probabilistic classifier to generate a merged probability distribution based on the plurality of probability distributions; and using the plurality of prediction sets to select the plurality of content tags, the plurality of content tags being associated with the first set of individuals. 23. The method of claim 22, further comprising: using a Monte Carlo method to generate the plurality of prediction sets based on the merged probability distribution, the prediction sets including a plurality of prediction values for the first set of individuals, each respective prediction value being associated with one of the plurality of content tags. 24. The method of claim 21, further comprising: determining that a plurality of new content items have been added to the plurality of content items; selecting an additional content item from the plurality of new content items based on the first content tag; modifying the display layout to include the additional content item; and transmitting, to a display device, the additional content item, the modified display layout, and a second display time, wherein, the display device is further configured to display the additional content item using the modified display layout, the additional content item being displayed for the new display time. 25. The method of claim 21, wherein the first feature comprises a hashtag. 26. The method of claim 21, wherein the tagging of the plurality of content items with the one or more of the plurality of content tags comprises tagging of the plurality of content items based on text included in one of the content items. 27. The method of claim 21, wherein the tagging of the plurality of content items with the one or more of the plurality of content tags comprises tagging of the plurality of content items based on an image included in one of the content items. 28. A system comprising: a display device adapted to display text and images; a memory adapted to store instructions; and one or more processors adapted to execute the instructions, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: identifying a plurality of individual data objects in a data store, each of the plurality of data objects defining features of a respective set of individuals; identifying a data object of the plurality of data objects, the data object identifying a first set of individuals having a first feature; selecting a plurality of content tags based on the first feature; tagging a plurality of content items with one or more of the plurality of content tags; identifying a first content item of the plurality of content items, the first content item being associated with a first content tag, the first content tag being associated with the first set of individuals; selecting a second content item based on the first content tag; generating a display layout for the first and second content items; and transmitting, to a display device, the first and second content item, the display layout, and a display time, wherein, the display device is configured to display the first and second content items using the display layout, the second content item being displayed for the display time. 29. The system of claim 28, wherein the selecting of the plurality of content tags comprises: for the first feature, determining a respective probability distribution indicating, for each of respective content tag among the plurality of content tags, a probability that a set of individuals associated with a content tag has the first feature, thereby generating a plurality of probability distributions; using a probabilistic classifier to generate a merged probability distribution based on the plurality of probability distributions; and using the plurality of prediction sets to select the plurality of content tags, the plurality of content tags being associated with the first set of individuals. 30. The system of claim 29, wherein the one or more processors are further adapted to use a Monte Carlo method to generate the plurality of prediction sets based on the merged probability distribution, the prediction sets including a plurality of prediction values for the first set of individuals, each respective prediction value being associated with one of the plurality of content tags. 31. The system of claim 28, wherein the first feature comprises a hashtag. 32. The system of claim 28, wherein the tagging of the plurality of content items with the one or more of the plurality of content tags comprises tagging of the plurality of content items based on text included in one of the content items. 33. The system of claim 28, wherein the tagging of the plurality of content items with the one or more of the plurality of content tags comprises tagging of the plurality of content items based on an image included in one of the content items. 34. The system of claim 28, wherein the one or more processors are further adapted to: determine that a plurality of new content items have been added to the plurality of content items; select an additional content item from the plurality of new content items based on the first content tag; modify the display layout to include the additional content item; and transmit, to a display device, the additional content item, the modified display layout, and a second display time, wherein, the display device is further configured to display the additional content item using the modified display layout, the additional content item being displayed for the new display time. 35. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon said execution, cause the one or more computers to perform operations comprising: identifying a plurality of individual data objects in a data store, each of the plurality of data objects defining features of a respective set of individuals; identifying a data object of the plurality of data objects, the data object identifying a first set of individuals having a first feature; selecting a plurality of content tags based on the first feature; tagging a plurality of content items with one or more of the plurality of content tags; identifying a first content item of the plurality of content items, the first content item being associated with a first content tag, the first content tag being associated with the first set of individuals; selecting a second content item based on the first content tag; generating a display layout for the first and second content items; and transmitting, to a display device, the first and second content item, the display layout, and a display time, wherein, the display device is configured to display the first and second content items using the display layout, the second content item being displayed for the display time. 36. The non-transitory computer-readable medium of claim 35, wherein the selecting of the plurality of content tags comprises: for the first feature, determining a respective probability distribution indicating, for each of respective content tag among the plurality of content tags, a probability that a set of individuals associated with a content tag has the first feature, thereby generating a plurality of probability distributions; using a probabilistic classifier to generate a merged probability distribution based on the plurality of probability distributions; and using the plurality of prediction sets to select the plurality of content tags, the plurality of content tags being associated with the first set of individuals. 37. The non-transitory computer-readable medium of claim 36, wherein the operations further comprise using a Monte Carlo method to generate the plurality of prediction sets based on the merged probability distribution, the prediction sets including a plurality of prediction values for the first set of individuals, each respective prediction value being associated with one of the plurality of content tags. 38. The non-transitory computer-readable medium of claim 35, wherein the operations further comprise: determining that a plurality of new content items have been added to the plurality of content items; selecting an additional content item from the plurality of new content items based on the first content tag; and modifying the display layout to include the additional content item; and transmitting, to a display device, the additional content item, the modified display layout, and a second display time, wherein, the display device is further configured to display the additional content item using the modified display layout, the additional content item being displayed for the new display time. 39. The non-transitory computer-readable medium of claim 35, wherein the first feature comprises a hashtag. 40. The non-transitory computer-readable medium of claim 35, wherein the tagging of the plurality of content items with the one or more of the plurality of content tags comprises tagging of the plurality of content items based on text included in one of the content items.