Richrelevance, Inc.

Sundeep Ahuja

David Lee Selinger

Tyler David Kohn

Michael James DeCourcey

James Osial

Albert Sunwoo

A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

System and process for improving product recommendations for use in providing personalized advertisements to retail customers

A method of improving user set recommendations for product advertising including receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products, receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.

System and process for improving recommendations for use in providing personalized advertisements to retail customers

A demand driven process of collecting structured product and service information including the steps of: receiving a request for information from a requestor about a specific product or service, or attribute or class thereof, associating one or more potential responders with the request for information, providing the request to the potential responders, receiving and organizing one or more responses, and providing the organized responses to the requestor, all electronically. An arrangement for the same includes user computers for receiving requests for information, network communication devices for transmitting the requests to potential responders, memory for storing electronic responses and a controller for organizing, updating, and providing access to the electronic responses.

Demand-driven, collaborative systems and processes for collecting structured information

A demand driven process of collecting and collaborating around reviews including the steps of: receiving a request for a review from a requestor, associating one or more potential responders with the request for a review, providing the request for a review to one or more potential responders, receiving and organizing one or more reviews from the responders, providing access to the organized reviews to the requestor and to the responders, and updating the accessible organized reviews as additional reviews are received, all electronically. An arrangement for the same includes user computers for receiving requests for a review, network communication devices for transmitting the requests to potential responders, memory for storing electronic reviews and a controller for organizing, updating, and providing access to the electronic reviews.

A system and process for incorporating recommendation boosting in an automated recommendation system includes receiving recommendation boost instructions, receiving a request for one or more recommendations, receiving a set of recommendations from one or more automated recommendation systems, with each recommendation system utilizing selection models or user models and modifying the set of product recommendations according to the recommendation boost instructions.

System and process for boosting recommendations for use in providing personalized advertisements to retail customers

A system and process for incorporating recommendation boosting in an automated recommendation system includes presenting a user with a visual electronic interface adapted to receive recommendation boost instructions regarding a boost subject, receiving recommendation boost instructions via the visual electronic interface, wherein the recommendation boost instructions indicate how strongly the boost subject should be recommended or suppressed from being recommended, receiving a set of recommendations from one or more automated product recommendation systems, wherein each recommendation system utilizes one or more selection models or user models and modifying the set of recommendations according to the recommendation boost instructions.

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Gold River, CA, United States of America

Sundeep Ahuja