Dynamic Neural Network for Venue Recommendation Ranking
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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 utilize dynamic user preferences and reviewer interactions.
Innovation Solution
A recommendation engine builds a neural network of interrelationships among venues, reviewers, and users based on attributes and reviews, using collaborative and content-based links to determine the strength of connections and generate recommendations dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use static indexing systems to rank web pages, then the system structure is simple and easy to maintain, but the accuracy of ranking and recommendations is insufficient because they cannot effectively utilize dynamic user preferences and reviewer interactions
Solution Approach 1:
The patent transforms the static indexing system into a dynamic neural network that continuously adapts to user preferences and reviewer interactions. The network structure allows weights and connections to be dynamically adjusted based on user feedback, enabling the system to evolve and improve ranking accuracy over time while maintaining manageable complexity through localized updates.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions and reviewer ratings are continuously fed back into the neural network to adjust connection weights and improve future recommendations. This feedback loop enables the system to learn from user behavior and progressively enhance ranking precision without requiring complete system reconfiguration.
2Reliability
If search engines update the entire indexing system to reflect changes in user preferences, then the recommendations remain accurate and relevant, but the computational cost and time required for updates increase significantly
Solution Approach 1:
The patent divides the large-scale indexing system into modular neural network units that can be independently updated. When user preferences change, only the affected local connections and weights need to be adjusted rather than reprocessing the entire system. This segmentation enables efficient, localized updates that maintain recommendation relevance while minimizing computational overhead and update time.
3Adaptability or versatility
If conventional systems rely on content-based matching only, then the implementation is straightforward and computationally efficient, but the ability to capture user preferences and reviewer affinities is limited
Solution Approach 1:
The patent merges content-based matching with collaborative filtering approaches by integrating them into a unified neural network architecture. The system combines traditional text analysis with user preference modeling and reviewer affinity detection, allowing it to capture both content relevance and user-specific preferences simultaneously. This integration enhances adaptability while managing complexity through the unified network structure.
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.