Item Embeddings for Consumer Need State Substitution
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Solution Overview
Problem
Current methods in retail environments rely on broad product categorization based on branding, which fails to capture the consumer decision-making process and consumer need states effectively, leading to inefficient product presentation and personalization.
Innovation Solution
A system that collects purchase history information, transforms it into a matrix of observed substitutions, and uses neural networks to generate item embeddings, allowing for the identification of item substitutions and need states, which are then used to optimize product placement, personalization, and promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If broad product categorization based on branding is used, then product presentation is simplified, but consumer decision-making process and need states are not captured effectively
Solution Approach 1:
The patent segments products into need states based on consumer decision-making processes rather than broad branding categories. This creates finer-grained groupings that reflect actual consumer behavior, allowing the system to capture substitution patterns and decision pathways while maintaining organized product presentation.
Solution Approach 2:
The patent introduces a new dimension for product categorization by mapping products into vector spaces based on consumer substitution behavior. This transforms the traditional single-dimension branding categorization into a multi-dimensional framework that captures nuanced consumer decision-making patterns and need states.
2Device complexity
If traditional product categorization methods are used, then system complexity is reduced, but personalization and product placement optimization are limited
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation layer between traditional product data and consumer behavior patterns. This intermediary enables the system to capture complex substitution relationships and need states without requiring direct complex rule-based systems, thus balancing complexity with personalization capability.
Solution Approach 2:
The patent transforms product categorization from static branding-based parameters to dynamic vector space parameters derived from consumer substitution behavior. This parameter transformation enables adaptive personalization and optimized product placement while maintaining manageable system complexity through mathematical transformations.
3Loss of time
If broad branding categories are used for product grouping, then data processing is simpler, but item substitution identification is less accurate
Solution Approach 1:
The patent performs preliminary action by pre-computing vector embeddings for products based on historical substitution data. This preprocessing step creates ready-to-use representations that enable fast real-time substitution identification without requiring complex calculations during actual product recommendations, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent creates vector space copies of product relationships that preserve substitution patterns in a compressed mathematical form. These vector representations serve as efficient copies of complex consumer behavior data, enabling accurate substitution identification with minimal processing time during runtime operations.
Data Source
AI summary
Systems and methods for identifying item substitutions. History information can be collected. The history information can include one or more episodes from one or more customers. Each episode can include one or more items. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. Input including an item can be received. A substitution similarity between the item and another item based on the item embeddings can be identified.