Multi-Context Similarity Detection for Product Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current product recommendation systems in electronic commerce venues require multiple iterations for users to converge on a final product selection, which can lead to decreased conversion rates due to inefficiencies in similarity assessment and recommendation processes.
Innovation Solution
A system utilizing a knowledge engine with a context manager that dynamically assesses multi-context similarity by combining vector maps with tensor representations and applying vector similarity algorithms to identify and re-assess product attributes, thereby reducing the number of iterations needed for product selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional product recommendation systems are used, then users can eventually find suitable products, but multiple iterations are required which decreases conversion rates
Solution Approach 1:
The system performs preliminary action by pre-computing vector maps and tensor representations for all products in the database before user interactions occur. This allows the recommendation system to quickly assess multi-context similarity without performing heavy computations during each interaction iteration, thereby reducing the number of iterations needed and improving conversion rates
Solution Approach 2:
The patent replaces traditional mechanical similarity assessment methods with cognitive computing approaches including vector similarity algorithms and tensor representations. This substitution enables more accurate and efficient similarity detection by leveraging mathematical models that capture multi-context relationships, reducing iteration requirements
2Measurement precision
If simple similarity assessment methods are used, then computation is faster, but accuracy in identifying similar products decreases
Solution Approach 1:
The system segments the similarity assessment process into distinct computational components: vector map generation, tensor representation creation, and vector similarity algorithm application. This segmentation allows each component to be optimized independently and cached for reuse, achieving high accuracy without proportionally increasing overall system complexity
Solution Approach 2:
The patent transforms product attributes into mathematical parameters through vector maps and tensor representations. By changing the representation parameters from raw product data to structured mathematical forms, the system enables efficient similarity computation while maintaining high measurement precision through the rich information captured in these representations
Data Source
AI summary
Embodiments relate to an intelligent computer platform for computing visual similarity and identification of a product responsive to the computed similarity. A first product is selected, and one or more product attributes are identified. A multi-context similarity is dynamically assessed combining a vector map with a tensor representation, and applying a vector similarity algorithm against the map and representation to identify one or more similar objects. In response to a second product selection, the multi-context similarity is dynamically re-assessed based on proximity to identify and select a final product.