14. A computer-implemented method, comprising: responsive to a present search query submitted by a user via a client-based application, returning personalized advertisements for the user from a server-based application to the client-based application, the personalized advertisements selected for return to the client-based application according to information regarding the user's interests, the personalized advertisements ranked in order of estimated user interest, the interest of the user estimated by applying identified properties of each respective one of the personalized advertisements to a user-specific learning machine to determine a probability of user interest for each respective personalized advertisement.
14. A computer-implemented method, comprising: responsive to a present search query submitted by a user via a client-based application, returning personalized advertisements for the user from a server-based application to the client-based application, the personalized advertisements selected for return to the client-based application according to information regarding the user's interests, the personalized advertisements ranked in order of estimated user interest, the interest of the user estimated by applying identified properties of each respective one of the personalized advertisements to a user-specific learning machine to determine a probability of user interest for each respective personalized advertisement. 17. The method of claim 14 , wherein the information regarding the user's interests is determined, in part, based on monitored user interactions with prior advertisements.