Mixed-Intent Basket Recommendations Using Product Type Codes
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Solution Overview
Problem
Existing systems fail to effectively recommend complementary items for mixed-intent baskets, such as those containing both groceries and general merchandise, leading to inefficient customer purchasing experiences.
Innovation Solution
A system that maps items in a mixed-intent basket to product type codes, generates a list of complementary product types, calculates scores for these types, and selectively recommends items based on their relevance and user interaction history to optimize the basket content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation systems are used for mixed-intent baskets, then the system structure remains simple, but the recommendation accuracy and customer purchasing experience deteriorate
Solution Approach 1:
The patent segments the recommendation system into multiple specialized components: a product type code assignment module that categorizes items, a complementary product type identification module that finds related types, and a recommendation generation module that produces suggestions. This segmentation allows each component to focus on a specific task, improving overall recommendation accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces product type codes as an intermediary layer between items and recommendations. Instead of directly analyzing item relationships, the system maps items to product type codes, then uses these codes to identify complementary product types. This intermediary approach simplifies the complexity of direct item-to-item relationship analysis while maintaining high recommendation accuracy.
2Quantity of substance
If complementary items are recommended for all items in the basket, then the quantity of recommendations increases, but the relevance and quality of suggestions deteriorate
Solution Approach 1:
The patent applies local quality by treating different items in the basket differently based on their product types. Instead of uniformly recommending complementary items for all products, the system identifies complementary product types specific to each item's category and generates targeted recommendations. This ensures that the quantity of recommendations is optimized while maintaining high relevance, as each suggestion is tailored to the specific local context of the item it accompanies.
3Productivity
If the system processes all items in the basket equally, then the processing time remains consistent, but the efficiency of generating relevant recommendations deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-assigning product type codes to items and pre-identifying complementary product type relationships. This preparation work is done in advance, allowing the recommendation generation process to quickly retrieve and utilize pre-computed information rather than performing complex analysis in real-time. This significantly improves recommendation generation efficiency while reducing processing time.
Data Source
AI summary
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: mapping each item of multiple items in a mixed-intent basket to a respective product type code (PT code); generating a respective list of complementary product type codes from each respective PT code; generating, using a complementary item algorithm, a respective candidate set of complementary items; detecting a platform-level configuration of a platform used by an electronic device of a user; loading, using diversity rotation, the respective quantity of complementary items onto a website carousel; and displaying the website carousel, as loaded, on the electronic device of the user, wherein the website carousel is sized to fit the platform-level configuration. Other embodiments are disclosed.