Nodal Relationship Network for Low-Resource Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engines require extensive computational resources and storage capacity to provide timely and accurate recommendations due to the large volume of data and immense number of combinations, leading to increased costs and inability to pre-calculate holistic searching.
Innovation Solution
A recommendation generator builds a network of interrelationships among venues, reviewers, and users based on their attributes and reviews, using a neural network architecture that dynamically updates and adjusts links based on user attributes and preferences, allowing efficient and localized processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines process large volume of data and immense number of combinations to provide holistic searching, then recommendation accuracy is improved, but computational resources and storage capacity requirements increase significantly
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 patterns and relationships, dividing the complex holistic searching task into manageable local computations. This segmentation allows the system to maintain high recommendation accuracy while reducing the computational burden on any single processing unit.
Solution Approach 2:
The patent transforms the traditional flat data processing approach into a multi-dimensional neural network structure where data flows through multiple layers of abstraction. By adding the dimensional aspect of hierarchical processing (input → hidden layers → output), the system can capture complex relationships without requiring exhaustive processing of all data combinations, thus improving accuracy efficiency.
2Reliability
If conventional systems continuously update and recalculate recommendations based on new data, then recommendation relevance is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent implements a dynamic neural network where connection weights between nodes are continuously adjusted based on incoming data and feedback. The system adapts its structure and parameters in real-time, allowing recommendations to remain relevant without requiring complete recalculation. This dynamic adjustment enables the system to maintain high relevance while reducing processing time through incremental updates.
Solution Approach 2:
The patent incorporates feedback mechanisms where the output of the neural network is fed back to adjust connection weights and node activations. This feedback loop allows the system to learn from previous recommendations and user interactions, continuously improving relevance without exhaustive recalculation. The feedback-driven adjustment enables efficient incremental updates that maintain high recommendation relevance.
3Measurement precision
If neural networks are used to process geographical location information and user attributes, then personalized recommendation accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by having different nodes in the neural network specialize in processing specific types of information (geographical location, user attributes, venue characteristics). Each node and layer focuses on local patterns within its domain, processing geographical data or user attributes with specialized logic. This local specialization improves personalized recommendation accuracy while managing system complexity through functional decomposition.
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.