Abstract: In one or more embodiments, one or more methods and/or systems described can perform receiving a pattern; determining that components of the received pattern match corresponding components of patterns that have not been rated by a user but have been rated by other users in the user's community; calculating multiple predictive ratings corresponding to the patterns; ranking the patterns based on the predictive ratings; and recommending one or more of the top-ranked patterns to the user. In one or more embodiments, calculating multiple predictive ratings corresponding to the patterns can include calculating multiple correlation coefficients. In one example, calculating multiple correlation coefficients can be based on the other users' ratings of the patterns. In another example, calculating multiple correlation coefficients can be based on the other users' ratings of one or more components of the patterns.

Abstract: A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents product page, configuration page, email and/or history using predetermined scoring criteria, such as item availability, item price, relevance of the item to the customer, item clarity, item exposure, total purchase amount in the cart, margin, recommendation confidence and lift, shopper profile matches, seasonality, customer history, time dependency, product marketing emphasis, substitutability, item popularity and/or product attributes associated with the recommended item. The scored recommendations may be ranked and/or filtered to prioritize the recommendations so that the best recommendation is selected for presentation to the customer. In addition, one or more scoring criteria or product attributes for the recommended item may be used to select a specific selling point/message text for inclusion with the selected recommendation.

Abstract: A data processing system normalizes data sets (such as low-resolution transaction data) into high-resolution data sets by mapping generic information into attribute-based specific information that is stored in a database. By establishing a shared domain model for representing items in the recommendation context, catalog and quote history with common terms and concepts, a recommendation engine operating in the shared domain may process the attribute-based representations to make specific and relevant recommendations to the customer. In addition, when certain attribute values are normalized over time, recommendations derived from past order history can be intelligently applied to current orders. The normalized representation of elements in the shared domain may also be used to generate compelling selling point text for each recommendation that is specific to the marketing objectives of the seller and identifies the objectives of the buyer.

Abstract: In a particular embodiment, configuration patterns are used to provide solutions to configuration problems that repetitively occur. Use of configuration patterns in connection with a configuration model has many benefits. A configuration patterns is a parameterized or otherwise easily extensible solution to that problem, such that the solution is reusable by modifying the parameters of the pattern or extending the pattern in pre-defined ways. Configuration patterns are inherently data-driven and provide improved maintenance for data and logic as well as reduced programming effort. Configuration patterns can be reused within and across different product families and can be specialized from other patterns. Thus, the use of configuration patterns facilitates reduced maintenance cost and reduced programming effort for product configuration data and logic systems.