Neural Network Recommendation Engine for Dynamic Relevance
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Solution Overview
Problem
Conventional search engines face challenges in accurately ranking web pages based on relevance and quality, as they rely on static indexing systems and limited methods for determining importance, which may not capture dynamic user preferences and relationships effectively.
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 approaches to dynamically update link strengths and generate recommendations that reflect user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use static indexing systems and limited methods for determining importance, then the system complexity is low, but the measurement precision of web page relevance and quality is insufficient
Solution Approach 1:
The patent transforms the static indexing system into a dynamic neural network that continuously learns and updates link strengths based on user interactions and preferences. The system adapts over time by modifying connection weights between nodes, enabling more precise relevance determination while managing complexity through incremental updates rather than complete system reconfiguration
Solution Approach 2:
The patent introduces intermediate nodes representing users, reviewers, and venues that mediate between query terms and web pages. These intermediary nodes capture implicit relationships and preferences, allowing the system to infer relevance through multiple hops in the neural network rather than direct matching, thereby improving measurement precision
2Reliability
If search engines rely on content matching and citation counting, then the ease of operation is maintained, but the reliability of relevance ranking is insufficient
Solution Approach 1:
The patent implements feedback loops where user interactions with search results, reviews, and preferences are continuously fed back into the neural network to adjust link strengths. This feedback mechanism improves reliability by incorporating real-world validation of relevance, while the automated nature of the feedback processing maintains ease of operation
Solution Approach 2:
The patent replaces traditional mechanical citation counting and content matching mechanisms with a neural network-based system that uses weighted connections and activation functions. This substitution enables more reliable relevance ranking through distributed representations and pattern recognition, while the underlying computational mechanisms remain automated and relatively simple to operate
3Measurement precision
If the neural network is updated globally in response to attribute changes, then the measurement precision is maintained, but the loss of time for system updates increases
Solution Approach 1:
The patent segments the neural network into localized regions or communities of nodes that can be updated independently. When attributes change, only the affected local neighborhoods are recalculated rather than the entire network, maintaining measurement precision in updated regions while dramatically reducing update time through parallel processing of discrete segments
Solution Approach 2:
The patent applies partial updates to only those portions of the neural network that are affected by attribute changes, rather than performing complete global updates. This partial action approach maintains sufficient measurement precision for affected regions while minimizing the time loss associated with system-wide recalculations
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.