Creation and various uses of an example model of preferences that displays certain types of time and history dependent dynamics are disclosed. Creation and use of the model may be based on insights from studies in human psychology and gained from the exploration of real world temporal preference data. Particularly, the dynamics of satiation for familiar content are incorporated in the model by dynamic item preference states. In some examples, the model may identify different latent preference states for items which are called the Sensitization, the Boredom, and the Recurrence states. Dynamics in a user's preferences for items may be attributed to the dynamics in these item states.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system comprising: a repository storing a plurality of content items; a web service having a content delivery engine to retrieve and communicate the content items to users over a computer network, wherein the web service maintains a dynamic user preference model comprising a plurality of states, wherein the dynamic user preference model accounts for temporal changes in content preferences of the users with respect to content items consumed by the users, and wherein, for each user, the web service models the dynamic preferences of the user according to the states of the model, generates, based on a current state associated with the user, a recommendation of at least one of the content items and outputs the recommendation to the user via the computer network. 2. The computing system of claim 1, wherein, for each user, the web service updates the model based on a respective frequency of consumption for the content items consumed by the respective user and the time elapsed to compute a result indicative of a rate of declining preference for consumption for the content items. 3. The computing system of claim 2, wherein the web service maintains the model to include sensitization and the recurrence preference states for the content items for each of the users, and wherein the model represents state dependent consumption rates (Ci(t)) for an item i for a user u with the elapsed time for a state si, given its frequency of consumption f as: C i  ( t  s i = Sensitization ) = LL  ( t , α s f , 1 ) 1 - cLL  ( t , α s f , 1 ) , and C i  ( t  s i = Recurrence ) = LL  ( t , α r f , β r f ) 1 - cLL  ( t , α r f , β r f ) . 4. The computing system of claim 1, wherein, for each user and based on the current state associated with the user, the web service organizes content items on a web pages provided to the particular user. 5. The computing system of claim 1, wherein, for each user and based on the current state associated with the user, the web service computes a predicted return time for the user that represents a computed estimate of a time in the future that the user is likely to return to the web service to request a content item that is the same or similar to a content item recently delivered to the user. 6. A method comprising: generating, by a computing device and based at least in part on data indicating previous actions of one or more users, a dynamic user preference model comprising a plurality of states, wherein the dynamic user preference model accounts for temporal changes in content preferences of a user; and executing, based at least in part on the dynamic user preference model, a programmatic action. 7. The method of claim 6, wherein executing the programmatic action comprises: determining, based at least in part on data indicating content consumed by a particular user, a state from the plurality of states to associate with the particular user; and generating, based on the state associated with the particular user, at least one content recommendation for the particular user. 8. The method of claim 6, wherein executing the programmatic action comprises: determining, based at least in part on data indicating content consumed by a particular user, a state from the plurality of states to associate with the particular user; and organizing, based on the state associated with the particular user, content items provided to the particular user for consumption. 9. The method of claim 6, further comprising: determining, based at least in part on data indicating content consumed by a particular user, a state from the plurality of states to associate with the particular user; and determining, based at least in part on the state associated with the particular user, a predicted retention of the particular user, wherein executing the programmatic action is further based at least in part on the predicted retention of the particular user. 10. The method of claim 9, wherein executing the programmatic action comprises generating at least one of: a strategic decision, a policy decision, or a site layout decision. 11. The method of claim 9, wherein determining a predicted retention of the particular user comprise computing a predicted return time for the user that represents a computed estimate of a time in the future that the user is likely to return to the web service and request a content item that is the same or similar to a content item recently delivered to the user. 12. A non-transitory computer-readable storage medium encoded with instructions that, when executed, cause at least one processor to: maintain, by a computing system and for each of a user that has previously consumed content items from a content repository from the computing system, a dynamic user preference model comprising a plurality of states, wherein the plurality of states of the dynamic user preference model models temporal changes in declining content preferences of the user over time with respect to the content items consumed by the user; determine, based on the model, that a current content preference of the user for the content items previously consumed by the user has devalued over time below a threshold; generate, responsive to the determination, a recommendation of a new one of the content items; and output the recommendation to the user via the computer network. 13. The non-transitory computer-readable storage medium of claim 12, wherein the instructions cause the computing system to: update, for each of the users, the model based on a respective frequency of consumption for the content items consumed by the respective user and a time elapsed since a last request for the content items from the user, and compute, for each of the users, a result indicative of a rate of declining preference for consumption for the content items. 14. The non-transitory computer-readable storage medium of claim 13, wherein the instructions cause the computing system to: maintain the model to include sensitization and the recurrence preference states for the content items for each of the users, and represent, within the model, state dependent consumption rates (Ci(t)) for an item i for a user u with the elapsed time for a state si, given its frequency of consumption f as: C i  ( t  s i = Sensitization ) = LL  ( t , α s f , 1 ) 1 - cLL  ( t , α s f , 1 ) , and C i  ( t  s i = Recurrence ) = LL  ( t , α r f , β r f ) 1 - cLL  ( t , α r f , β r f ) . 15. The non-transitory computer-readable storage medium of claim 13, wherein the threshold is computed as: P(si=Sensitization)<Td and P(si=Recurrence)<Td→si=Boredom.