Systems and methods for recommending cold-start items on a website of a retailer
First Claim
1. A system comprising:
- one or more processing modules; and
one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of;
training, using a historical traffic pattern of a plurality of items on a website of an online retailer, one or more first models to recommend one or more first items of the plurality of items for sale on the website of the online retailer after a user has had an interaction on the website of the online retailer with one or more second items of the plurality of items;
determining static features common to both the one or more first items and the one or more second items;
training, using the static features of the one or more first items and the one or more second items, a second model to determine whether to coordinate a display of any new item as one of one or more recommended items with any of the plurality of items;
determining, using the second model and one or more static features of the new item, whether to coordinate a display of the new item as one of the one or more recommended items when one or more of the plurality of items are displayed on the website of the online retailer; and
coordinating the display of the new item as the one of the one or more recommended items when the one or more of the plurality of items are displayed on the website of the online retailer based on using the second model and the one or more static features of the new item.
2 Assignments
0 Petitions
Accused Products
Abstract
Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training one or more first models to recommend a first item after a user has had an interaction on the web site of the online retailer with a second item, determining static features common to both the first item and the second item, training a second model to determine whether to coordinate a display of any new item as one of one or more recommended items with any of a plurality of items, and coordinating the display of the new item as one of the one or more recommended items when the one or more of the plurality of items are displayed on the website of the online retailer based on the static features of the new item.
10 Citations
20 Claims
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1. A system comprising:
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one or more processing modules; and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of; training, using a historical traffic pattern of a plurality of items on a website of an online retailer, one or more first models to recommend one or more first items of the plurality of items for sale on the website of the online retailer after a user has had an interaction on the website of the online retailer with one or more second items of the plurality of items; determining static features common to both the one or more first items and the one or more second items; training, using the static features of the one or more first items and the one or more second items, a second model to determine whether to coordinate a display of any new item as one of one or more recommended items with any of the plurality of items; determining, using the second model and one or more static features of the new item, whether to coordinate a display of the new item as one of the one or more recommended items when one or more of the plurality of items are displayed on the website of the online retailer; and coordinating the display of the new item as the one of the one or more recommended items when the one or more of the plurality of items are displayed on the website of the online retailer based on using the second model and the one or more static features of the new item. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method comprising:
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training, using a historical traffic pattern of a plurality of items on a website of an online retailer, one or more first models to recommend one or more first items of the plurality of items for sale on the website of the online retailer after a user has had an interaction on the website of the online retailer with one or more second items of the plurality of items; determining static features common to both the one or more first items and the one or more second items; training, using the static features of the one or more first items and the one or more second items, a second model to determine whether to coordinate a display of any new item as one of one or more recommended items with any of the plurality of items; determining, using the second model and one or more static features of the new item, whether to coordinate a display of the new item as one of the one or more recommended items when one or more of the plurality of items are displayed on the website of the online retailer; and coordinating the display of the new item as the one of the one or more recommended items when the one or more of the plurality of items are displayed on the website of the online retailer based on using the second model and the one or more static features of the new item. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification