Context-Aware Alternative Product Identification From Metadata
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying suitable alternative products due to vast amounts of information and computational inefficiencies, particularly in retail and e-commerce scenarios, leading to confusion and inefficient time management for retailers and customers.
Innovation Solution
A method and system that standardizes and translates metadata from retailer and competitor products, uses binary classification to filter relevant data, calculates lexical and semantic similarities, and applies a machine learning model to rank alternative products based on feature importance scores, optimizing computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If e-commerce engines use content-based filtering and collaborative filtering to provide product options, then the quantity of alternative products increases, but the confusion of retailers and customers increases
Solution Approach 1:
The patent applies parameter changes by transforming the product comparison from unstructured metadata to structured numerical features. It extracts specific product attributes (price, specifications, features) and converts them into comparable parameters, enabling systematic ranking and filtering of alternatives rather than presenting all possible options.
Solution Approach 2:
The patent implements local quality by focusing on specific important attributes of products rather than treating all products uniformly. It identifies and weights key product features (price, specifications, customer ratings) to evaluate alternatives locally based on their relevance to the original product, providing targeted recommendations rather than generic lists.
2Measurement precision
If detailed product information is made available in brick and mortar retail shops, then the accuracy of product comparison improves, but the time management efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical comparison processes with automated computational systems. It uses machine learning models and algorithms to automatically extract, compare, and rank product alternatives based on multiple attributes, substituting human time-consuming manual analysis with rapid automated processing that maintains high accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-processing and structuring product data before comparison is needed. It extracts and organizes product metadata, specifications, and attributes in advance, creating a ready-to-use structured format that enables rapid comparison when customers need alternatives, eliminating the need for real-time information gathering.
3Measurement precision
If competitive intelligence tools are used to track competitor products, then the information accuracy about competitors improves, but the computational time and resources increase
Solution Approach 1:
The patent applies extraction by selectively extracting only the most relevant product attributes and metadata needed for comparison, rather than processing all available competitor information. It identifies and extracts key features (product specifications, pricing, ratings) from competitor data, filtering out unnecessary information to reduce computational burden while maintaining accuracy.
Solution Approach 2:
The patent implements partial action by focusing computational resources on analyzing only the most critical product attributes and a limited set of key competitors. Rather than comprehensively analyzing all competitor products in detail, it performs partial analysis on essential features that drive comparison decisions, reducing overall computational requirements.
Data Source
AI summary
This disclosure relates generally to a method and system for identifying alternative products from competitor products having closer similarity to a retailer product. State-of-the-art methods for alternative product identification based on collaborative filtering often yield inappropriate matches as they do not consider context relevance. Moreover, due to the large size of dataset, such methods require more computation time. While making a comparison of retailer product with the competitor product, a context aware comparison as well as achieving sizable dataset is not yet achieved. The present disclosure addresses these problems through a method of processing metadata of the retailer product and the competitor products to derive feature importance score. The processing results in a filtered data set comprising unique retailer-competitor product pairs. The filtered data is then processed by a machine learning algorithm to identify alternative products based on closest similarity with the retailer product and accordingly ranked.