Versata Development Group, Inc.

Trilogy Development Group, Inc.

Thomas H Dillon

James Singh

Neil C Thier

Sherrill J Packebush

Timothy Paul Darr

Jeremy S Wacksman

Michael K Grant

Alex Holm Devine

Chris Hyams

Nirad Sharma

Jacob D Leffler

A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents using by assessing and ranking each candidate recommendation by the expected incremental margin associated with the recommendation being issued (as compared to the expected margin associated with the recommendation not being issued) by taking into consideration historical associations, knowledge of the layout of the site, the complexity of the product being sold, the user's session behavior, the quality of the selling point messages, product life cycle, substitutability, demographics and/or other considerations relating to the customer purchase environment. In an illustrative implementation, scoring inputs for each candidate recommendation (such as relevance, exposure, clarity and/or pitch strength) are included in a probabilistic framework (such as a Bayesian network) to score the effectiveness of the candidate recommendation and/or associated selling point messages by comparing a recommendation outcome (e.g., purchase likelihood or expected margin resulting from a given recommendation) against a non-recommendation outcome (e.g., the purchase likelihood or expected margin if no recommendation is issued). In addition, a probabilistic framework may also be used to select a selling point message for inclusion with a selected candidate recommendation by assessing the relative strength of the selling point messages by factoring in a user profile match factor (e.g., the relative likelihood that the customer matches the various user case profiles).

Scoring recommendations and explanations with a probabilistic user model

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.

System and method for generating effective recommendations

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.

Retail recommendation domain model

Balancing toy

A system and method for product configuration represents a product using a configuration model having numerical clauses. Features of the product are associated with literals in the numerical clauses. The numerical clauses may specify a number of literals to determine whether the numerical clause is satisfied, satisfiable, or unsatisfiable. Numerical clauses represent constraints that govern the configuration of the product. Resolving numerical clauses and user choices involves one or more inference procedures. In one embodiment, unit clauses represent user choices, and a configuration engine asserts the unit clause and performs numerical unit resolution between the unit clause and all numerical clauses containing the complement (generally a negation) of the unit clause. Complementary literals are eliminated from the current state of the configuration model. In one embodiment, if the uneliminated literals in a numerical clause causes the clause to be satisfied, the uneliminated literals are recursively asserted. The configuration engine then performs numerical unit resolution between the recursively asserted, uneliminated literals and numerical clauses containing the complements of the uneliminated literals. Numerical clauses may also function as literals. Numerical clauses may include negations of consumer literals and positive provider literals. If a user chooses a consumer feature, by knowing the consumer and provider weights, the configuration engine can determine which provider literals should be included, excluded, or remain selectable. Embodiments of the invention can implement a logic-based truth maintenance system and numerical unit resolution.

System and method for product configuration using numerical clauses and inference procedures

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Austin, TX, United States of America

Thomas H Dillon