Machine Learning Image Arrangement to Reduce Webpage Latency
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Solution Overview
Problem
Websites face limitations in displaying a large number of content items due to user interface size, bandwidth, and memory restrictions, leading to significant latency or inability to provide content, which affects interaction rates with the target audience.
Innovation Solution
A neural network is used to select a smaller number of content items likely to draw the target audience's attention, generating a benchmark for comparison with input images, and only the top-performing images are uploaded to avoid latency issues while maintaining interaction rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of content items are displayed on the webpage, then the interaction rate with the target audience increases, but the network bandwidth and system memory are overwhelmed causing significant latency
Solution Approach 1:
The patent applies partial action by using a neural network to evaluate and select only a subset of content items that are most likely to engage the target audience, rather than displaying all available content items. This selective approach maintains high interaction rates while reducing the total number of items processed and displayed, thereby decreasing latency and bandwidth consumption.
Solution Approach 2:
The system performs preliminary evaluation of content items using a neural network before they are displayed on the webpage. This pre-screening process identifies and ranks content items based on their likelihood to engage the target audience, allowing the system to prepare and serve only the most relevant items, thus reducing processing time and latency during actual display.
2Productivity
If a large number of content items are displayed on the webpage, then the interaction rate with the target audience increases, but the user interface size and memory restrictions prevent effective display
Solution Approach 1:
The patent implements partial action by selectively displaying only a portion of available content items that have been evaluated and ranked by the neural network. This approach ensures that the user interface displays the most relevant content without being overwhelmed by the total volume of available items, maintaining simplicity while maximizing interaction potential.
3Loss of energy
If fewer content items are displayed on the webpage, then network bandwidth and system resources are preserved, but the interaction rate with the target audience decreases
Solution Approach 1:
The patent changes the parameter of content selection from displaying all items to displaying only high-scoring items based on neural network evaluation. By transforming the selection criterion from quantity-based to quality-based (using interaction probability as the parameter), the system achieves efficient bandwidth usage while maintaining or improving interaction rates through smarter content curation.
Data Source
AI summary
A method includes receiving a pointer to a plurality of images of a web page, the web page configured to display the plurality of images in a first arrangement; retrieving, using the pointer, the plurality of images of the web page; executing at least one machine learning model using the plurality of images from the web page as input to generate at least one image performance score for each image of the plurality of images, the at least one machine learning model trained based on a training set of images labeled based at least on interaction data corresponding to images of the training set of images; and rearranging the plurality of images on the web page to a second arrangement according to the at least one image performance score generated for each image of the plurality of images of the web page.