Data-Driven Product Classifier Using Contextual Transaction Embeddings
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Solution Overview
Problem
Manual product classification in retail industries often leads to poor decision-making due to inaccuracies, resulting in significant lost revenue, as it relies on expert presumptions rather than data-driven insights.
Innovation Solution
Implementing a data-driven classifier system that processes transaction datasets using a contextualizing algorithm, such as Word2Vec, to generate data representations of product contexts, followed by clustering algorithms like K-means to identify product clusters based on affinity and relations, thereby automating the classification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual product classification by catalog experts is used, then product categorization can be performed a priori based on product character and purpose, but classification accuracy deteriorates leading to poor decision-making and lost revenue
Solution Approach 1:
The patent replaces the manual mechanical classification process performed by catalog experts with an automated data-driven system using machine learning algorithms. The system processes transaction datasets to generate data representations and applies clustering algorithms to automatically partition products into clusters, eliminating human subjectivity and improving classification accuracy while maintaining ease of operation.
Solution Approach 2:
The classification system performs self-service by automatically analyzing transaction data and generating product classifications without requiring continuous human intervention. The data-driven classifier autonomously processes datasets, generates data representations, and partitions products into clusters based on learned patterns from transaction data, making the system self-sufficient while improving accuracy.
2Measurement precision
If data-driven classification with contextualizing algorithms is implemented, then classification accuracy improves by identifying product contexts and relationships, but system complexity increases
Solution Approach 1:
The patent segments the classification process into distinct modular components: a data contextualizing algorithm that generates data representations from transaction datasets, and a clustering algorithm that partitions products based on these representations. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high classification accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces data representations as an intermediary between raw transaction data and final product classifications. The contextualizing algorithm transforms transaction data into structured data representations that capture product contexts and relationships, which then serve as input for the clustering algorithm. This intermediary layer simplifies the overall process by preprocessing and structuring information before clustering, reducing the complexity of direct classification from raw data.
3Productivity
If automated clustering algorithms are used to partition products into clusters, then productivity increases by automating the classification process, but measurement precision may deteriorate without expert oversight
Solution Approach 1:
The patent implements a feedback mechanism where the data-driven classifier continuously processes transaction datasets and refines product classifications based on learned patterns. The system generates data representations from transaction data, applies clustering to partition products, and can iteratively improve classifications by reprocessing data with updated cluster assignments. This feedback loop maintains high accuracy while achieving automated high-throughput processing, eliminating the trade-off between productivity and precision.
Data Source
AI summary
One method embodiment includes receiving a transaction dataset including data representative of transactions including data representative of at least one product purchased within the respective transactions. This method then processes the dataset according to a contextualizing algorithm to generate a data representation for at least some products included in transactions of the transaction dataset. Each generated data representation represents a context of a product with regard to each of the other products of the data representation. This method further includes processing the generated data representations according to a clustering algorithm to partition products represented by the generated data representations into a number of product clusters. A data representation of the product clusters may then be stored including data identifying products and the product clusters to which they are partitioned.