Richrelevance, Inc.

Tyler David Kohn

David Lee Selinger

Michael James DeCourcey

Sundeep Ahuja

Albert Sunwoo

James Osial

Darren Erik Vengroff

Randall Stuart Fish

Murtaza Doctor

Bradley Ross Cerenzia

Jason Zien

Techniques are described related to selecting content items, such as by enabling user analysis and control of product-related content items selected for display to users. The content items may include advertisements or other promotional materials, and the selecting of the content items may be performed as part of determining particular promotional materials to display or otherwise present to particular users in particular situations. User analysis and control of selected content items that are displayed on a target electronic site may be enabled by providing, as part of the target electronic site, additional selection-related functionality whose availability is restricted to one or more authorized users&#x2014;for example, such additional restricted access information and user-selectable controls may be provided on a version of a Web page of an online retailer to enable the retailer to analyze and influence future content items selected for display on the online retailer's Web page(s).

Contextual analysis and control of content item selection

Techniques are described for determining personalized category-based product information for a user. The described techniques may in some situations be used by or on behalf of an online retailer to determine a sorted order of at least some products within a product category for the online retailer to display to a customer user on a category-specific Web page of the online retailer, such as based on a browse request by the customer for that category or on another identification of the category by the customer. The determination of particular personalized category-based sorted products for such a customer may in some situations be performed based at least in part on prior activities of the customer, including prior interactions by the customer with the online retailer (e.g., actions by the customer to view and/or purchase particular products, product brands, product sub-categories, product sizes, etc.).

Performing personalized category-based product sorting

An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface. An arrangement for determining the relative strength of a classification for a group of words includes memory for storing a contextual relationship graph for a given classification and a processor that receives the contextual relationship graph and a plurality of words to be analyzed by the processor, identifies occurrences of the relationships identified in the contextual relationship graph and determines the relative strength of classification based on the identified occurrences.

System and method for providing targeted content

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

Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI ('graphical user interface') for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g., newly generated recommendations that are similar to the selected items in one or more manners, or are otherwise related to the selected items).

Multi-strategy generation of product recommendations

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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San Francisco, CA, United States of America

Tyler David Kohn