Microindustry Clustering via Feature Vector Analysis
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Solution Overview
Problem
Social networking systems face challenges in effectively identifying and presenting microindustries within vast arrays of user and entity profiles, leading to reduced visibility and missed opportunities for connections and collaborations.
Innovation Solution
A clustering machine within the social networking system generates microindustry clusters by analyzing user and entity profiles, using feature vectors, movement data, and graph algorithms to identify interrelations and present tailored user interface presentations, revealing previously undetected similarities and associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social networking systems gather and track vast arrays of user behavior information, then the system can analyze data to generate solutions to various problems, but the complexity of data processing and analysis increases significantly
Solution Approach 1:
The patent introduces clustering machines as intermediary components that process raw user behavior data and transform it into structured cluster information. These clustering machines act as mediators between the data collection system and the application layer, handling the complex processing tasks of analyzing user profiles, entity profiles, and behavior patterns to generate meaningful clusters without burdening the overall system architecture
Solution Approach 2:
The system implements self-service mechanisms where the clustering machines automatically generate and update cluster information based on incoming data without requiring manual intervention. The clustering process autonomously processes user profiles, entity profiles, and behavior data to create and maintain microindustry clusters, allowing the system to serve itself in terms of data organization and classification
2Ease of operation
If the system presents tailored user interface presentations based on microindustry clusters, then user experience and connection opportunities improve, but the computational resources required for real-time clustering and presentation increase
Solution Approach 1:
The system performs preliminary clustering actions by pre-processing user profiles, entity profiles, and behavior data to generate cluster information in advance. The clustering machines create and maintain microindustry clusters proactively, so that when users interact with the system, the tailored presentations can be generated from pre-computed cluster data rather than performing complex clustering operations in real-time
Solution Approach 2:
The patent segments the large-scale data processing task into smaller, manageable components by creating distinct clustering machines for different data types (user profile clustering, entity profile clustering, behavior data clustering). This segmentation allows parallel processing and distributes computational load, reducing the energy consumption required for any single clustering operation while enabling comprehensive data analysis
Data Source
AI summary
Methods and systems for generating tailored user interface presentations based on microindustry clustering. According to various embodiments, the system accesses a set of entity profiles and a set of member profiles. The system determines a set of feature vectors for each entity of the set of entity profiles and identifies a set of movement data representing changes in association of one or more members from a first entity to a second entity. The system generates an entity graph for the set of entities and the set of members. The systems generate a first set of clusters in the entity graph, a second set of clusters by partitioning one or more of the first clusters, and a set of third clusters from the set of second clusters, combining one or more of the second clusters.