Context-Aware Content Ranking via Social Graph Analysis
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Solution Overview
Problem
Conventional search result ranking methods, such as those used by Google and social media platforms, do not effectively incorporate user context and social graph interactions to prioritize search results and content delivery, limiting relevance and user experience.
Innovation Solution
A method and system that utilize a user's social graph to detect interactive behavior and contextual affinity, assigning weighting factors to rank search results and content based on both contextual affinity and inbound links, constructing a multi-dimensional object matrix to indicate interactive behavior and categorize relation types for enhanced relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search results are ranked based on inbound links only, then the ranking is simple and fast, but the relevance to user context and social interactions is poor
Solution Approach 1:
The patent combines multiple ranking signals including inbound links, user context data, and social graph interactions into a unified ranking system. This merging of diverse data sources enables more precise measurement of search result relevance while systematically managing the complexity through integrated processing.
Solution Approach 2:
The ranking system is designed to handle multiple types of data (inbound links, contextual information, social interactions) through a single multi-functional framework. This universal approach allows the system to process diverse inputs and generate comprehensive relevance scores without requiring separate specialized systems for each data type.
2Adaptability or versatility
If user context and social graph interactions are incorporated into search result ranking, then the relevance and user experience are improved, but the system complexity and processing requirements increase
Solution Approach 1:
The system segments the complex task of search result ranking into distinct components: inbound link analysis, user context processing, and social graph interaction evaluation. Each segment handles specific types of data independently before their results are integrated, making the overall system more manageable and adaptable.
Solution Approach 2:
The ranking system dynamically adjusts weights and parameters based on user context and social interactions rather than using fixed rules. This dynamic adaptation enables personalized content delivery while the modular architecture manages complexity through flexible, adjustable components rather than rigid structures.
Data Source
AI summary
A prioritized list of items available via an electronic user device is provided to a user. The user has relations categorized in a social graph for which activity is crawled to detect interactive behavior with objects made by the relations via respective electronic devices. The prioritizing includes identifying items for the list; determining a relative level of the user's contextual affinity with one or more of the list items, contextual affinity to a list item characterized by connectedness of a context of the user's current use of the electronic user device to a manner in which a relation has had interactive behavior with one of the objects that corresponds to the list item; and ranking the list items according to the relative levels of affinity.