US 12,393,972 B2
Method, system, and medium for attribute-based item ranking during a web session
Congzhe Su, Irvine, CA (US); Amey Barapatre, Bellevue, WA (US); Xiaoting Zhao, Queens, NY (US); Diane Hu, Brooklyn, NY (US); and Xu Liu, Bellevue, WA (US)
Assigned to Etsy, Inc., Brooklyn, NY (US)
Filed by Etsy, Inc., Brooklyn, NY (US)
Filed on Feb. 28, 2022, as Appl. No. 17/682,366.
Claims priority of provisional application 63/154,790, filed on Feb. 28, 2021.
Prior Publication US 2022/0277375 A1, Sep. 1, 2022
Int. Cl. G06Q 30/0601 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06Q 30/0627 (2013.01); G06Q 30/0641 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer implemented method to improve ranking performance of items during a user web session, comprising:
providing, by a processor and during the user web session, a first content page for display on a client device, wherein the first content page includes a first plurality of items;
receiving, by the processor and during the user web session, a set of user interactions with one or more of the first plurality of items;
generating, using a first machine learning model operating on data representing the first plurality of items, a first set of attributes for the first plurality of items;
training, during the user web session, a second machine learning model to generate affinity scores, wherein training the second machine learning model comprises:
training a multi-armed bandit (MAB) using the set of user interactions from the user web session to learn attributes of interest, wherein each arm of the MAB represents one attribute of the first set of attributes, and wherein training the MAB comprises adjusting, using (i) a set of training data including the set of user interactions from the user web session and (ii) a corresponding set of labels representing positive or non-positive user interactions, a first score associated with a first attribute of the first set of attributes;
determining, using (i) the second machine learning model, (ii) the set of user interactions, and (iii) the first set of attributes for the first plurality of items, an affinity score representing a user interest in each attribute of the first set of attributes;
in response to a request by a user of the user web session for additional items during the user web session, identifying a second plurality of items;
providing, to the first machine learning model, data representing the second plurality of items;
generating, using the first machine learning model operating on the data representing the second plurality of items, a second set of attributes for the second plurality of items;
generating, using (i) the second set of attributes and (ii) the affinity scores corresponding to the first set of attributes, a set of ranking scores for the second plurality of items;
ranking, during the user web session and using the set of ranking scores for the second plurality of items, the second plurality of items; and
providing, during the user web session, a second content page for display on the client device, wherein the second content page includes the ranked second plurality of items.