Neural Network Recommendation Engine with Feedback-Updated Nodal Relationships
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Solution Overview
Problem
Existing search engines require extensive computational resources and storage capacity to provide timely and accurate recommendations due to the large volume of data, and cannot pre-calculate holistic searching results effectively.
Innovation Solution
A recommendation engine builds a network of interrelationships among venues, reviewers, and users based on their attributes and reviews, using a neural network to generate recommendations by aggregating link matrices and determining the most strongly coupled venues, with efficient localized updating capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines process large volume of data to provide accurate recommendations, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent segments the neural network into multiple layers (input layer, hidden layers, output layer) with nodes distributed across these layers. Each node processes local information and passes results to connected nodes, dividing the overall computational task into smaller, parallelizable units that reduce energy consumption while maintaining recommendation accuracy.
Solution Approach 2:
The system pre-calculates and stores interrelationship strengths between nodes in advance, building the neural network structure beforehand. This preliminary action allows the system to quickly retrieve pre-computed relationships during actual recommendation generation, avoiding energy-intensive real-time calculations while preserving measurement precision.
2Reliability
If search engines perform holistic searching to provide comprehensive results, then reliability is improved, but productivity decreases
Solution Approach 1:
The neural network structure and interrelationship strengths are built and stored in advance through pre-processing of venue attributes, user profiles, and review data. This preliminary construction enables the system to deliver comprehensive recommendation results quickly by simply traversing the pre-built network rather than performing holistic searching at query time.
Solution Approach 2:
The system dynamically adjusts the neural network structure by adding or removing nodes and modifying interrelationship strengths based on incoming data (new venues, users, or reviews). This dynamic adaptation maintains comprehensive and reliable recommendations while keeping the system responsive and efficient without requiring complete re-processing of all data.
3Adaptability or versatility
If the system updates recommendations in real-time based on user feedback, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system incorporates user feedback by updating interrelationship strengths between nodes based on user interactions and preferences. When users provide feedback or exhibit certain behaviors, the system adjusts the weights of connections in the neural network, enabling adaptive recommendations while maintaining a relatively simple architecture that only requires modifying existing node relationships rather than adding complex control mechanisms.
Solution Approach 2:
The neural network performs self-updating through automated adjustment of interrelationship strengths based on incoming data and user feedback. The system automatically recalculates and modifies node connections without requiring manual intervention or complex external control systems, achieving adaptability while minimizing device complexity.
Data Source
AI summary
A neural network connects entities, individuals, and attributes with a first subset of nodes each corresponding to a respective entity, a second subset of nodes each corresponding to a respective individual, and a third subset of nodes each corresponding to a respective attribute. The connections of the neural network each reflect a strength of an interrelationship between at least two nodes. Responsive to a user requesting results corresponding to one or more entities, the neural network is used to identify one or more results based on nodal connections. The user may submit, through a user interface, at least one feedback indication, each characterizing approval or disapproval of a respective result. The neural network may be updated, using the feedback indication(s), by adjusting the strength of interrelationship reflected by strengths of one or more of the connections.