Ensemble Learning for Product Recommendation Accuracy
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Solution Overview
Problem
Current methods for providing targeted content, such as targeted advertising, face challenges in accurately classifying recipient context and predicting relevant non-competitive advertisements across retailers, leading to inefficiencies and limitations in scalability and flexibility.
Innovation Solution
The system utilizes a hierarchical predictive projection with contextual relationship graphs and ensemble learning to select targeted content based on user behavior and product data, enabling real-time analysis and adaptation without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the bucket of words approach is used for context classification, then the processing speed is fast, but the accuracy of context classification deteriorates
Solution Approach 1:
The patent introduces an intermediary mapping layer that connects bucket-of-words context classifications to product recommendation classifications. This mapping mechanism translates simple keyword-based context into structured product category relationships, enabling accurate recommendations without requiring complex natural language processing while maintaining fast processing speeds.
2Measurement precision
If natural language processing is used for context classification, then the accuracy of context classification improves, but the processing speed deteriorates
Solution Approach 1:
The patent extracts only the essential contextual elements needed for product recommendations from web page content, rather than performing complete natural language processing. By taking out only the relevant keywords and mapping them to product categories, the system achieves sufficient accuracy for advertising purposes while avoiding the computational overhead of full NLP analysis.
3Measurement precision
If prefiltering is applied in natural language processing, then the accuracy of contextual extraction improves, but the productivity deteriorates due to human involvement
Solution Approach 1:
The patent implements automated keyword extraction and mapping algorithms that perform prefiltering without human intervention. The system automatically identifies relevant keywords, maps them to product categories, and generates advertising recommendations autonomously, eliminating the need for manual prefiltering while maintaining high accuracy and improving processing efficiency.
4Measurement precision
If classification trees for web pages and advertisements are made congruous, then the matching accuracy improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal product classification taxonomy that serves both web page categorization and advertisement matching purposes. This single unified classification system handles multiple functions - organizing web content, categorizing products, and enabling ad targeting - thereby improving matching accuracy without requiring separate complex classification trees for each function.
Data Source
AI summary
A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.