Intelligent Product Recommendation System Using Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing product recommendation systems rely heavily on consumer selection patterns and fail to accurately predict product performance for incremental responses, as they do not account for disparate consumer conditions and complex product performance, leading to unreliable recommendations.
Innovation Solution
A system utilizing neural networks and collaborative/content-based filters that process multivariate data to provide individualized product recommendations based on objective and subjective feedback, optimizing segmentation and performance-based learning to predict consumer preferences and product performance accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation systems use consumer selection patterns, then they can provide product recommendations, but they fail to accurately predict product performance for incremental responses
Solution Approach 1:
The patent segments consumers into distinct groups based on their conditions, characteristics, and responses to products. By dividing the consumer population into segments with similar profiles, the system can provide more accurate performance predictions for each segment rather than using generic selection patterns, thereby improving measurement precision while maintaining recommendation reliability
Solution Approach 2:
The patent changes the parameters used for recommendations from simple selection patterns to multivariate data including consumer conditions, product attributes, and performance metrics. This parameter transformation enables accurate prediction of incremental responses by considering multiple factors simultaneously, resolving the contradiction between prediction accuracy and recommendation reliability
2Measurement precision
If the system collects and processes multivariate data from consumers, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces neural networks as intermediary components that automatically process and analyze multivariate consumer data. These neural network intermediaries handle the complexity of processing multiple data variables, extracting meaningful patterns, and generating accurate recommendations, thereby improving recommendation accuracy while managing system complexity through automated intelligence
Solution Approach 2:
The patent replaces traditional mechanical recommendation systems with neural network-based intelligent systems. This substitution allows the system to automatically process complex multivariate data without requiring manual configuration or simple rule-based logic, enabling high recommendation accuracy while the neural networks manage the computational complexity internally
3Adaptability or versatility
If the system uses neural networks and collaborative filtering, then it can handle complex product performance prediction, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training neural networks with extensive consumer data and product information before actual recommendation generation. This pre-processing allows the system to quickly make accurate predictions during runtime, enhancing adaptability for complex product performance prediction while reducing processing time when recommendations are actually needed
Solution Approach 2:
The patent implements collaborative filtering and neural network processing selectively for products and consumers where complex prediction is most beneficial, rather than applying these computationally intensive methods uniformly to all recommendations. This partial application reduces overall processing time while maintaining high adaptability for complex product performance prediction cases
Data Source
AI summary
Systems and methods of utilizing communications networks and multivariate analysis to predict or recommend optimal products from a predefined population of commercially available products are disclosed. The recommendations are based on intelligence contained in processing elements and subjective and/or objective product information received from consumers or input to the systems as part of their initial setup. The output of the systems comprise sets of products that they predict the consumer will prefer and/or perform well for the problem or concern identified by the consumer. The performance and preference predictions are a function of consumer problems and product responsiveness patterns. Objective product information is generally obtained with diagnostic instruments. Data measured with the diagnostic instruments may be communicated to the data processing portions of the invention via the Internet. The outputs of the data processing portion of the system may be presented to consumers via the Internet as well.