Abstract: Systems and methods for evaluating keyword performance include receiving history data indicative of online actions performed regarding one or more advertisements. A set of one or more keywords may be associated with the one or more advertisements. A performance metric for the set of one or more keywords may be determined using the history data, as well as a confidence interval for the performance metric. Confidence intervals for the performance metrics of different sets of keywords may be sorted and provided.

Abstract: Shown is an improved method and system for implementing and building messages, such as HL7-based messages. Messages are constructed in a format-independent manner, in which a message is built by instantiating and linking Java classes. Metadata for the message specifications are used to construct the Java class libraries. Signature of the classes automatically cause the messages to be constructed with correct syntax, structure, and restrictions. Thus, the correctness of the constructed messages can be enforced at compile time. Moreover, a developer can construct a specification-specific message without having to know XML.

Abstract: An automated recommendation system keeps track of the needs and preferences of the user through a user preference vector. Each field of the user preference vector represents the user's preference for a specific item attribute. Item attributes are defined by a systems programmer. The systems programmer also creates product vectors of items in the recommendation database. A user preference vector is compared against a product vector to determine if the product is suitable for recommendation. A recommended item may be purchased by the user by submitting a purchase request over a network connection. The user preference vector is constantly refined through feedback from the user about the recommended items.