GENERATION OF PRODUCT RECOMMENDATIONS USING A DYNAMICALLY SELECTED STRATEGY

• US 20120310771A1
• Filed: 08/13/2012
• Published: 12/06/2012
• Est. Priority Date: 03/31/2009
• Status: Abandoned Application

• Current Assignee: RichRelevance Incorporated
• Original Assignee: RichRelevance Incorporated
• Inventors: Selinger, David Lee, DeCourcey, Michael James, Fish, Randall Stuart, Cerenzia, Bradley Ross, Kohn, Tyler David, Vengroff, Darren Erik
• Application Number:
  US13/584,611
• Publication Number:
  US 20120310771A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0643 Graphical representation of items or shoppers

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).

Claims

1-40. -40. (canceled)

41. A configured computing system comprising:

one or more processors; and

a product recommendation system that is configured to, when executed by at least one of the one or more processors, provide recommendations to multiple users by, for each of the users;

obtaining information that reflects prior interactions of a plurality of consumers with a plurality of products;

receiving additional information about a current situation involving the user interacting with one or more first products available from a retailer;

automatically determining one or more products of the plurality of products to recommend to the user in the current situation based on multiple distinct recommendation strategies, the determining including se one of the multiple recommendation strategies to use for the current situation, and further including selecting determined one or more products from product recommendations generated by the selected one recommendation strategy, the product recommendations being generated by the selected one recommendation strategy using information about the one or more first products and using the information about the prior interactions of the plurality of consumers with the plurality of products; and

providing information about the determined one or more products for use as part of a Web page from the retailer for display to the user.

42. The computing system of claim 41 wherein the providing of the recommendations to the multiple users further includes, for one of the multiple users, and after the providing of the information about the determined one or more products for use as part of the Web page from the retailer for display to the one user:

receiving an indication of a selection of at least one of e determined one or more products by the one user via the Web page

determining a group of multiple additional products of the plurality of products to recommend to the one user using a combination of the multiple recommendation strategies, the determined group of multiple additional products being based at least in part on the selected at least one product; and

providing further information about the multiple additional products of the determined group for use as part of an updated Web page for display to the one user on a client device of the one user.

43. The computing system of claim 42 further comprising the client device of the one user, the one-client device being configured to receive the Web page for display to the one user, to display the Web page to the one user such that the determined one or more products are displayed as part of the Web page, to receive the selection of the at least one product by the one user via the Web page, to send the indication of the selection to the product recommendation system, to receive the provided further information about the multiple additional products of the determined group, and to dynamically modify the displayed Web page, without reloading the displayed Web page, to include information about the multiple additional products of the determined group.

44. The computing system of claim 41 wherein the providing of the recommendations to the multiple users further includes, for one of the multiple users, after the providing of the information about the determined one or more products for display to the one user, initiating an order from the retailers for the one user of one of the determined one or more products after the one user selects that one product for purchase via one or more interactions with the Web page displayed to the user.

45. The computing system of claim 41 wherein the product recommendation system includes software instructions for execution by the at least processors of the computing system.

46. The computing system of claim 41 wherein the product recommendation system consists of one or more means for performing the providing of the recommendations to the multiple users.

47. The computing system of claim 41 wherein, for each of one or more of the multiple users, the selecting of the one recommendation strategy to use for the current situation includes:

for each of the multiple recommendation strategies, generating at least one product recommendation for the current situation based on the one or more first products and on the information about the prior interactions of the plurality of users with the plurality of products; and

identifying the selected one recommendation strategy based at least in part on comparing information about the generated at least one product recommendation for each of the multiple recommendation strategies.

48. The computing system of claim 41 wherein, for a first user of the multiple users, the selected one recommendation strategy to use for the current situation involving the first user is a first strategy of the multiple recommendation strategies, and for a distinct second user of the multiple users, the selected one recommendation strategy to use for the current situation involving the second user is a second strategy of the multiple recommendation strategies that is distinct from the first strategy.

49. A computer-implemented method comprising:

obtaining, by one or more configured computing systems, information that reflects prior interactions of a plurality of users with a plurality of products;

receiving, by the one or more configured computing systems, information about a situation involving a first user and one or more first products;

determining, by the one or more configured computing systems, one or more products of the plurality of products to recommend to the first user based on multiple distinct recommendation strategies, the determining including selecting one of the multiple recommendation strategies to use for the situation, and further including selecting the determined one or more products from product recommendations generated by the selected one recommendation strategy, the product recommendations being generated by the selected one recommendation strategy using information about the one or more first products and using the information about the prior interactions of the plurality of users with the plurality of products; and

providing information about the determined one or more products for presentation to the first user.

50. The method of claim 49 wherein the selecting of the one recommendation strategy to use for the situation includes:

for each of the multiple recommendation strategies, generating at least one product recommendation for the situation based on at least one of the one or more first products and of the information about the prior interactions of the plurality of users with the plurality of products; and

identifying the selected one recommendation strategy based at least in part on comparing information about the generated at least one product recommendation for each of the multiple recommendation strategies.

51. The method of claim 49 wherein the selecting of the one recommendation strategy to use for the situation includes identifying the one recommendation strategy as being predetermined for use with the one or more first products.

52. The method of claim 49 wherein the selected one recommendation strategy generates the product recommendations from a subset of the plurality of products that are identified as popular based at least in part on the prior interactions of the plurality of users with the plurality of products.

53. The method of claim 49 wherein the selected one recommendation strategy to use for the situation involving the first user is a first strategy of the multiple recommendation strategies, and wherein the method further comprises determining, by the one or more configured computing systems, one or more second products of the plurality of products to recommend to a second user based on selecting a second strategy of the multiple recommendation strategies that is distinct from the first strategy and based on selecting the determined one or more second products from product recommendations generated by the second strategy.

54. The method of claim 49 wherein generating of the product recommendations by the selected one recommendation strategy further uses information specific to the first user.

55. The method of claim 49 wherein generating of the product recommendations by the selected one recommendation strategy is further based at least in part on feedback from the first user related to at least one product.

56. The method of claim 49 wherein the provided information about the determined one or more products is for use as part of a Web page for display to the first user on a client device of the first user, and wherein the method further comprises, after the providing of the information about the determined one or more products:

receiving an indication of a selection of at least one of the determined one or more products by the first user via the Web page;

determining a group of multiple additional products of the plurality of products to recommend to the first user using a combination of the multiple recommendation strategies, the determined group of multiple additional products being based at least in part on the selected at least one product; and

providing further information about the multiple additional products of the determined group for display to the first user on the client device of the first user.

57. The method of claim 49 wherein the multiple recommendation strategies are each one of identifying products that are popular for a particular category of products, of identifying products that are popular for a particular retailer of products, of identifying products that are similar to the one or more first products, of identifying products that are related to the one or more first products based at least in part on the prior interactions of the plurality of users, and of identifying products with which one or more users have indicated to group the one or more first products.

58. The method of claim 49 wherein the multiple recommendation strategies include each of identifying products that are popular for a particular category of products to which the one or more first products belong, of identifying products that are popular for a particular retailer of products, of identifying products that are similar to the one or more first products based at least in part on those identified products each sharing one or more attributes with the one or more first products, of identifying products that are related to the one or more first products based at least in part on users who interact with the one or more first products in one or more manners also interacting with those identified products in the one or more manners, and of identifying products with which one or more users have indicated to group the one or more first products.

59. The method of claim 49 wherein the one or more first products correspond to one of multiple product categories, and wherein the selected one recommendation strategy includes at least one of selecting products that are frequently purchased from the one product category and of selecting products that are frequently viewed from the one product category.

60. The method of claim 49 wherein the one or more first products correspond to at least one of multiple search results of a search initiated by the first user, and wherein the selected one recommendation strategy includes at least one of selecting products that are similar to the one of more first products and of selecting products that are related to the one or more first products in the prior interactions of the plurality of users.

61. The method of claim 49 wherein the one or more first products are indicated by the first user, and wherein the selected one recommendation strategy includes at least one of selecting products that are similar to the one or more first products and of selecting products that are related to the one or more first products in the prior interactions of the plurality of users.

62. The method of claim 49 wherein the plurality of users are customers of an online retailer, wherein the information that reflects the prior interactions of the plurality of users with the plurality of products is obtained based at least in part on tracking interactions of the plurality of customers with the online retailer, wherein the one or more configured computing systems provide a multi-strategy product recommendation service that determines product recommendations for multiple customer users of the online retailer, and wherein the method further comprises automatically initiating an order from the online retailer for the first user of one of the determined one or more products after the first user selects that one determined product for purchase via one or more interactions with the provided information about the determined one or more products.

63. A non-transitory computer-readable medium having stored contents that configure a computing device to perform a method, the method comprising:

obtaining, by the configured computing device, information that reflects prior interactions of a plurality of users with a plurality of items;

receiving, by the configured computing device, information about a first user and one or more first items;

determining, by the configured computing device, one or more items of the plurality of items to recommend to the first user based on multiple distinct recommendation strategies, the determining including selecting one of the multiple recommendation strategies to use and further including selecting the determined one or more items from item recommendations generated by the selected one recommendation strategy, the item recommendations being generated by the selected one recommendation strategy using the information about the prior interactions of the plurality of users with the plurality of items; and

providing information about the determined one or more items for presentation to the first user.

64. The non-transitory computer-readable medium of claim 63 wherein the one or more first items are products, and wherein generating of the item recommendations by the selected one recommendation strategy further uses information about the one or more first items.

65. The non-transitory computer-readable medium of claim 63 wherein generating of the item recommendations by the selected one recommendation strategy further uses information about the first user.

66. The non-transitory computer-readable medium of claim 63 wherein the selecting of the one recommendation strategy to use includes:

for each of the multiple recommendation strategies, generating at least one item recommendation based on at least one of the one or more first items and of the information about the prior interactions of the plurality of users with the plurality of items; and

identifying the selected one recommendation strategy based at least in part on comparing information about the generated at least one item recommendation for each of the multiple recommendation strategies.

67. The non-transitory computer-readable medium of claim 63 wherein the multiple recommendation strategies are each one of identifying items that are popular for a particular category of items, of identifying items that are popular for a particular retailer, of identifying items that are similar to the one or more first items, of identifying items that are related to the one or more first items based at least in part on the prior interactions of the plurality of users, and of identifying items with which one or more users have indicated to group the one or more first items.

68. The non-transitory computer-readable medium of claim 63 wherein the multiple recommendation strategies include each of identifying items that are popular for a particular category of items to which the one or more first items belong, of identifying items that are popular for a particular retailer, of identifying items that are similar to the one or more first items based at least in part on those identified items each sharing one or more attributes with the one or more first items, of identifying items that are related to the one or more first items based at least in part on users who interact with the one or more first items in one or more manners also interacting with those identified items in the one or more manners, and of identifying items with which one or more users have indicated to group the one or more first items.

69. The non-transitory computer-readable medium of claim 63 wherein the plurality of users are customers of an online retailer, wherein the information that reflects the prior interactions of the plurality of users with the plurality of products is obtained based at least in part on tracking interactions of the plurality of customers with the online retailer, and wherein the configured computing device is part of a multi-strategy product recommendation service that determines item recommendations for multiple customer users of the online retailer.

70. The non-transitory computer-readable medium of claim 63 wherein the computer-readable medium is a memory of the configured computing device, and wherein the stored contents are software instructions that, when executed, program the configured computing device to perform the method.