Recommendation Engine Using Dynamic Neural Network Link Matrices
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Solution Overview
Problem
Conventional search engines face challenges in accurately ranking and recommending web pages based on user preferences and venue attributes, as they rely on static indexing systems and lack dynamic resonance between user reviews and venue interactions.
Innovation Solution
A recommendation engine builds a neural network of interrelationships among venues, reviewers, and users based on attributes and reviews, using dynamic resonance and geometric contextualization to determine recommended venues, allowing for efficient updating and personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use static indexing systems to rank web pages, then the system complexity is low, but the recommendation accuracy and relevance to user preferences deteriorates
Solution Approach 1:
The patent transforms the static indexing system into a dynamic neural network that continuously learns and adapts from user interactions, reviewer feedback, and venue attributes. The system updates link matrices and nodal relationships in real-time, allowing recommendations to evolve with user preferences rather than relying on fixed pre-computed indexes.
Solution Approach 2:
The patent introduces neural networks and link matrices as intermediary structures between users and venues. These intermediaries process and aggregate complex relationships among users, reviewers, and venues, enabling accurate recommendations without requiring direct complex queries to the underlying data.
2Adaptability or versatility
If search engines rely on basic content matching and link structure, then the ease of operation is high, but the ability to capture dynamic user preferences and reviewer interactions deteriorates
Solution Approach 1:
The patent extends the traditional two-dimensional search space (query-document matching) to multiple dimensions by incorporating user attributes, reviewer opinions, venue characteristics, and interaction histories. This multi-dimensional approach enables the system to capture complex user preferences and contextual relationships that simple content matching cannot detect.
Solution Approach 2:
The system pre-computes and stores link matrices, nodal relationships, and neural network weights based on historical data, user interactions, and reviewer feedback. These pre-computed structures enable rapid adaptation to new queries without requiring complex real-time computations, maintaining operational efficiency while capturing dynamic preferences.
3Productivity
If conventional systems use comprehensive indexing of all web pages, then the coverage is complete, but the time and computational resources required for processing deteriorates
Solution Approach 1:
The patent segments the comprehensive indexing task into modular neural network components, including user-venue link matrices, reviewer-venue relationships, and attribute-based connections. This segmentation allows parallel processing and localized updates, significantly improving processing efficiency compared to monolithic indexing systems that must process entire datasets sequentially.
Solution Approach 2:
The system dynamically adjusts parameters such as link matrix weights, neural network activation thresholds, and aggregation coefficients based on data freshness, user engagement patterns, and computational resource availability. This enables the system to optimize processing time while maintaining comprehensive coverage by focusing computational effort on the most relevant and recently updated relationships.
Data Source
AI summary
In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.