NLP Product Substitution System Using Semantic Vector Mapping
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Solution Overview
Problem
Existing product substitution methods lack effectiveness in recommending substitute products based on nuanced relationships between products, as they fail to accurately capture contextual data and consumer behavior, leading to suboptimal marketing and sales strategies.
Innovation Solution
A product substitution system utilizing natural language processing (NLP) algorithms, such as Word2Vec, to generate multi-dimensional representations of product relationships, determine product-to-product affinities, and recommend substitute products by analyzing descriptive purchased product data from retailers, enabling personalized digital promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional product substitution methods are used, then implementation is simple, but recommendation accuracy and effectiveness are poor
Solution Approach 1:
The patent replaces traditional mechanical/product-based substitution methods with an NLP-based semantic analysis system. By using Word2Vec and other NLP techniques, the system transforms product relationship analysis from a simple categorical matching problem into a sophisticated semantic understanding task, significantly improving recommendation accuracy while accepting increased system complexity
Solution Approach 2:
The patent changes the fundamental parameters of product relationship analysis by moving from discrete product categories and simple attributes to continuous semantic vectors in multi-dimensional space. This parameter transformation enables nuanced understanding of product relationships, allowing the system to capture subtle affinities and contextual relationships that traditional methods miss
2Measurement precision
If NLP algorithms are used to analyze product relationships, then recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements pre-computation of product semantic vectors and maintains ready-to-use embeddings for products. By performing the computationally intensive NLP processing in advance and storing the results, the system can quickly retrieve and compare product affinities during actual substitution scenarios, significantly reducing real-time processing time while maintaining high accuracy
Solution Approach 2:
The patent applies partial NLP processing by focusing computational resources on the most relevant aspects of product descriptions. Rather than analyzing every possible attribute equally, the system identifies and processes key semantic features that most strongly influence product substitution decisions, reducing overall computational burden while preserving recommendation quality
3Measurement precision
If detailed descriptive product data is analyzed, then substitution recommendations become more accurate, but data processing complexity increases
Solution Approach 1:
The patent extracts and isolates the most critical semantic features from detailed product descriptions, separating essential information from redundant details. By focusing only on the key attributes and semantic elements that drive substitution decisions, the system maintains high recommendation accuracy while reducing the complexity of data processing and feature engineering
Data Source
AI summary
A product substitution system may include a shopper device associated with a given shopper, and a product substitution server. The server may obtain descriptive purchased product data for purchased products from a retailer, and iteratively operate a natural language processing (NLP) algorithm to accept, as input thereto, the descriptive purchased product data, and generate, as output therefrom, a multi-dimensional representation of a relationship among the purchased products. The server may also, for each iteration, determine a product-to-product affinity based upon the multi-dimensional representation of the relationship, and determine a recommended substitute product for one of the products based upon the determined product-to-product affinity, and communicate the recommended substitute product to the shopper device. The server may also, for each iteration, obtain updated descriptive purchased product data for an updated plurality of purchased products from the retailer.