Recommendation Engine Using Spatial Segmentation for Data Clustering
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Solution Overview
Problem
Current search engine systems face challenges in providing timely and accurate recommendations without requiring extensive computational resources, especially when processing large volumes of data for holistic searching, and they struggle to pre-calculate information due to the vast number of combinations and varieties of data types.
Innovation Solution
A recommendation engine builds a network of interrelationships among venues, reviewers, and users based on attributes and reviews, using spatial segmentation and neural networks to efficiently update and provide recommendations by aggregating link matrices and applying user attribute weights, allowing for localized updates and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines process large volumes of data for holistic searching, then the accuracy and completeness of recommendations is improved, but the computational resource requirements and processing time increase significantly
Solution Approach 1:
The patent segments the search space into multiple clusters based on data characteristics. Each cluster is processed independently by dedicated processing units, allowing parallel computation. This segmentation enables the system to handle large volumes of data without requiring excessive computational resources, as each segment can be processed with localized resources rather than requiring full-system processing of all data.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing data into clustered structures before actual search queries are executed. Data is pre-segmented into meaningful groups, and relationships between data points are pre-established within clusters. This preliminary organization allows queries to be answered more efficiently by leveraging the pre-computed cluster structures rather than processing raw data from scratch for each query.
2Speed
If search engines attempt to pre-calculate information for holistic searching, then the speed of providing recommendations is improved, but the complexity and resource requirements for handling vast combinations increase
Solution Approach 1:
The patent divides the complex search space into multiple manageable clusters, each representing a specific domain or category of data. This segmentation transforms the overwhelming task of pre-calculating all possible information into smaller, tractable sub-tasks that can be handled by individual processing units. Each cluster maintains its own internal relationships without requiring exhaustive analysis of all possible combinations across the entire dataset.
Solution Approach 2:
The system performs partial pre-calculation by computing only the necessary relationships within each cluster rather than attempting to pre-calculate all possible information. The clustering approach allows the system to focus computational effort on locally relevant data relationships, providing sufficient information for accurate recommendations without the excessive complexity of complete pre-computation across all data combinations.
3Loss of time
If conventional systems process all available data in real-time, then the timeliness of recommendations is improved, but the computational cost and energy consumption increase
Solution Approach 1:
The patent implements segmentation by dividing the data processing task into multiple parallel cluster processing operations. Each processing unit handles a specific cluster independently and simultaneously, enabling real-time recommendation generation without requiring sequential processing of all data. This parallel segmented processing achieves timeliness while distributing computational load, thereby reducing overall energy consumption compared to centralized real-time processing of all data.
Solution Approach 2:
Each data cluster is self-sufficient in that it contains the necessary information and relationships to generate recommendations independently. The clustering structure enables each segment to serve its own processing needs without requiring constant access to or processing of the entire dataset. This self-service capability allows timely recommendations to be generated from local cluster data, reducing the energy required for comprehensive real-time data processing.
Data Source
AI summary
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.