Dynamic Neural Network for Venue Recommendation
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Solution Overview
Problem
Conventional search engines face challenges in accurately ranking and recommending web pages based on user queries, as they rely on static indexing systems and do not effectively capture dynamic user preferences and venue relationships.
Innovation Solution
A recommendation engine that builds a neural network of interrelationships among venues, reviewers, and users based on attributes and reviews, using collaborative and content-based links to generate recommendations by aggregating link matrices and determining strongly coupled venues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional search engines use static indexing systems to rank web pages, then the system structure is simple and easy to implement, but the system cannot effectively capture dynamic user preferences and venue relationships
Solution Approach 1:
The patent transforms the static indexing system into a dynamic neural network that continuously learns and adapts to user preferences and venue relationships. The system evolves from fixed page rankings to adaptive link matrices that update based on user behavior, reviewer feedback, and changing preferences, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The patent introduces neural networks and link matrices as intermediary structures between the search engine and web pages. These intermediaries capture complex user preferences and venue relationships that cannot be directly represented in traditional indexing systems, enabling dynamic adaptation while maintaining a manageable system architecture.
2Measurement precision
If search engines use comprehensive content analysis to determine page relevance, then the relevance determination is thorough and accurate, but the processing time and computational resources increase
Solution Approach 1:
The patent pre-computes link matrices and venue preferences during off-peak times, storing processed relationship data for rapid retrieval during search operations. This preliminary action separates the computationally intensive analysis phase from the query response phase, maintaining high accuracy while reducing real-time processing time.
Solution Approach 2:
The patent divides the comprehensive content analysis into separate modular components: content-based link matrices, collaborative filtering matrices, and user preference profiles. Each segment can be independently computed and updated, allowing parallel processing that reduces overall computation time while maintaining thorough relevance determination.
3Stability of the object's composition
If the neural network is updated globally in response to attribute changes, then the network maintains consistency, but the updating process is computationally expensive and time-consuming
Solution Approach 1:
The patent implements localized updating that modifies only the specific portions of the neural network affected by attribute changes, rather than re-computing the entire network. When a venue or user attribute changes, only the related link matrices and connections are updated, maintaining network consistency while dramatically improving updating efficiency.
Solution Approach 2:
The patent segments the neural network into independent updateable modules (user profiles, venue attributes, link matrices) that can be updated independently. This segmentation allows partial updates that maintain overall network consistency while avoiding the computational expense of global re-computation.
Data Source
AI summary
In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on 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.