View source for 18623691. MULTI-PATH COMPLIMENTARY ITEMS RECOMMENDATIONS simplified abstract (Walmart Apollo, LLC)

=MULTI-PATH COMPLIMENTARY ITEMS RECOMMENDATIONS=


==Organization Name==

[[:Category:Walmart Apollo, LLC|Walmart Apollo, LLC]]


[[Category:Walmart Apollo, LLC]]

==Inventor(s)==

[[:Category:Luyi Ma of Sunnyvale CA (US)|Luyi Ma of Sunnyvale CA (US)]][[Category:Luyi Ma of Sunnyvale CA (US)]]

[[:Category:Hyun Duk Cho of San Francisco CA (US)|Hyun Duk Cho of San Francisco CA (US)]][[Category:Hyun Duk Cho of San Francisco CA (US)]]

[[:Category:Sushant Kumar of Sunnyvale CA (US)|Sushant Kumar of Sunnyvale CA (US)]][[Category:Sushant Kumar of Sunnyvale CA (US)]]

[[:Category:Kannan Achan of Saratoga CA (US)|Kannan Achan of Saratoga CA (US)]][[Category:Kannan Achan of Saratoga CA (US)]]

==MULTI-PATH COMPLIMENTARY ITEMS RECOMMENDATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18623691 titled 'MULTI-PATH COMPLIMENTARY ITEMS RECOMMENDATIONS

The abstract describes a system that utilizes machine-learning models to generate personalized product-type metrics for users based on their historical activity and product-type pairs in an item taxonomy. The system then determines top product types for the user using an anchor item, identifies a set of first items associated with the top product types, ranks these items, and selects personalized complementary item recommendations for the user based on the anchor item.

* Utilizes machine-learning models to generate personalized product-type metrics for users
* Determines top product types for users based on an anchor item
* Ranks and selects personalized complementary item recommendations for users
* Based on historical user activity and product-type pairs in an item taxonomy
* Enhances user experience by providing tailored product recommendations

Potential Applications:
- E-commerce platforms for personalized product recommendations
- Online retail websites for enhancing user engagement
- Marketing strategies for targeted product promotions

Problems Solved:
- Improving user experience by offering personalized recommendations
- Enhancing user engagement and satisfaction
- Increasing sales and customer retention rates

Benefits:
- Personalized shopping experience for users
- Increased user satisfaction and engagement
- Higher conversion rates and sales for businesses

Commercial Applications:
Title: Personalized Product Recommendations System
This technology can be applied in e-commerce platforms, online retail websites, and marketing strategies to provide personalized product recommendations, enhance user engagement, and increase sales.

Questions about Personalized Product Recommendations System:
1. How does the system determine the top product types for users?
The system determines the top product types for users based on an anchor item and historical user activity.
2. What are the benefits of using machine-learning models in generating personalized product-type metrics?
Machine-learning models enhance the accuracy and relevance of personalized recommendations for users.


==Original Abstract Submitted==

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a trained machine-learning model, personalized product-type metrics for a user based on historic activity of the user and product-type pairs in an item taxonomy; determining top product types for the user based on an anchor item; determining a set of first items associated with the top product types; ranking each item in the set of first items for (i) the anchor item and (ii) for each item in the set of first items; and selecting, based on the ranking, a set of top items from the set of first items to be personalized complementary item recommendations for the user based on the anchor item. Other embodiments are described.



[[Category:G06Q30/0601]]
[[Category:G06F18/22]]
[[Category:G06N7/01]]
[[Category:CPC_G06Q30/0631]]