US 12,217,297 B2
Hyper-segmented personalization using machine learning-based models in an information processing system
Dhilip S. Kumar, Bangalore (IN); Sujit Kumar Sahoo, Bangalore (IN); Ajay Maikhuri, Bangalore (IN); and Devaraj Marappa, Krishnagiri District (IN)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on May 17, 2022, as Appl. No. 17/746,034.
Prior Publication US 2023/0377019 A1, Nov. 23, 2023
Int. Cl. G06Q 30/06 (2023.01); G06N 7/01 (2023.01); G06N 20/20 (2019.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06N 7/01 (2023.01); G06N 20/20 (2019.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
a processing platform comprising at least one processor coupled to at least one memory, the processing platform, when executing program code, being configured to:
identify, using one or more machine learning models, one or more similarities between product experience data of a given user and product experience data of one or more users to generate one or more product experience recommendations for the given user,
identify, using the one or more machine learning models, one or more similarities between purchase experience data of the given user and purchase experience data of the one or more users to generate one or more purchase experience recommendations for the given user;
generate a first machine learning model comprising at least one or more product experience recommendation data sets respectively from one or more product entities, wherein each of the one or more product experience recommendation data sets corresponds to one or more products produced by a respective one of the one or more product entities and is based on the one or more product experience recommendations for the given user with respect to at least one of the one or more products;
generate a second machine learning model comprising at least one or more purchase experience recommendation data sets respectively from one or more commerce entities, wherein each of the one or more purchase experience recommendation data sets corresponds to the one or more products sold by a respective one of the one or more commerce entities and is based on the one or more purchase experience recommendations for the given user with respect to at least one of the one or more products;
apply a federated ensemble-based machine learning algorithm to a ground truth label, at least one of the one or more purchase experience recommendation data sets and at least one of the one or more product experience recommendation data sets to generate and train a personalized model, wherein the application of the federated ensemble-based machine learning algorithm comprises:
aggregating the first machine learning model and the second machine learning model into the personalized model by combining each of the one or more product experience recommendation data sets with each of the one or more purchase experience recommendation data sets; and
determining one or more adaptations to be implemented on a purchasing interface of at least one of the one or more commerce entities with respect to a given one of the one or more users based on the personalized model to recommend one or more products to the given one of the one or more users; and
cause the one or more adaptations to be maintained wherein the given one of the one or more users is enabled to switch from the purchasing interface of at least one of the one or more commercial entities to another purchasing interface of at least another of the one or more commercial entities.