Meme-Based Supplemental Content Filtering for Resource-Efficient Delivery
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Solution Overview
Problem
Content providers inefficiently use computing and network resources to transmit supplemental content that is not of interest to users, as they do not account for the popularity or relevance of content related to memes being viewed, making it difficult to provide relevant supplemental data in real time.
Innovation Solution
Systems and methods for identifying memes and their categories using machine learning models, determining popularity, and providing supplemental content only if it is relevant and popular, conserving resources by selectively adding content to a data structure based on user-generated variations and similarity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content providers transmit supplemental content based on webpage category alone, then content delivery is simplified, but resource utilization becomes inefficient
Solution Approach 1:
The system transitions from using only category parameters to incorporating multiple parameters including meme popularity metrics, user engagement data, and content relevance scores. This multi-parameter approach enables selective content delivery that conserves resources by avoiding transmission of irrelevant supplemental content while maintaining operational simplicity through automated parameter-based filtering.
2Quantity of substance
If content providers provide supplemental content to all users viewing memes, then user coverage is maximized, but relevance to individual users decreases
Solution Approach 1:
The system applies local quality by tailoring supplemental content to specific user contexts based on their viewing history, engagement patterns, and preferences. Instead of uniform content delivery, the system adjusts content characteristics locally for each user segment, ensuring high relevance while maintaining broad coverage through automated classification and targeting mechanisms.
3Measurement precision
If content providers analyze meme popularity and user preferences in real-time, then content relevance improves, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-classifying memes into categories and pre-calculating popularity metrics before user interaction occurs. This advance processing stores processed data in accessible formats, enabling rapid relevance determination during actual content delivery without requiring complex real-time analysis, thus reducing operational complexity while maintaining precision.
4Quantity of substance
If content providers transmit supplemental content without filtering by popularity, then content availability increases, but waste of computing resources increases
Solution Approach 1:
The system extracts and separates supplemental content transmission based on popularity thresholds and relevance criteria. By filtering out content that fails to meet minimum popularity or relevance thresholds before transmission, the system maintains adequate content availability for qualified items while eliminating waste of computing resources on irrelevant content delivery.
Data Source
AI summary
Systems and methods are described for leveraging various computer-implemented techniques and/or data structures of memes, including root memes and their associated variants, to selectively provide supplemental content to a user using a computing device at which a meme is identified for presentation. The disclosed techniques may generate the supplemental content based on data associated with a plurality of memes, wherein the data may be indicative of at least one category associated with visual content of the respective memes. The disclosed methods may identify supplemental content related to the category of the meme. The disclosed techniques may cause the supplemental content to be provided for presentation in the vicinity of the meme at the display of the computing device.