David Lee Selinger - Castro Valley CA, US Michael James DeCourcey - Belmont CA, US Randall Stuart Fish - Castro Valley CA, US Bradley Ross Cerenzia - Seattle WA, US Tyler David Kohn - San Francisco CA, US Darren Erik Vengroff - Seattle WA, US
Assignee:
RichRelevance, Inc. - San Francisco CA
International Classification:
G06Q 40/00
US Classification:
705 711, 705 712
Abstract:
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).
Generation Of Product Recommendations Using A Dynamically Selected Strategy
David Lee Selinger - Castro Valley CA, US Michael James DeCourcey - Belmont CA, US Randall Stuart Fish - Castro Valley CA, US Bradley Ross Cerenzia - Seattle WA, US Tyler David Kohn - San Francisco CA, US Darren Erik Vengroff - Seattle WA, US
Assignee:
RICHRELEVANCE, INC. - San Francisco CA
International Classification:
G06Q 30/00
US Classification:
705 267
Abstract:
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).
Generating Display Information Using A Dynamically Selected Strategy
- San Francisco CA, US Michael James DeCourcey - Belmont CA, US Randall Stuart Fish - Castro Valley CA, US Bradley Ross Cerenzia - Seattle WA, US Tyler David Kohn - San Francisco CA, US Darren Erik Vengroff - Seattle WA, US
International Classification:
G06Q 30/06 G06Q 30/02
Abstract:
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
- San Francisco CA, US Michael James DeCourcey - Belmont CA, US Randall Stuart Fish - Castro Valley CA, US Bradley Ross Cerenzia - Seattle WA, US Tyler David Kohn - San Francisco CA, US Darren Erik Vengroff - Seattle WA, US
International Classification:
G06Q 30/06
Abstract:
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).
Randall Fish 1970 graduate of McCluer High School in Florissant, MO is on Memory Lane. Get caught up with Randall and other high school alumni from McCluer