Predictive Web Interface Pre-loading via Machine Learning
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Solution Overview
Problem
Large web environments experience significant latency due to the time required for the backend to determine user actions, retrieve necessary data, load it into cache, and process it for display, leading to perceived lag or latency for users.
Innovation Solution
A machine learning model is trained to predict a user's next action based on their history and current behavior, allowing data required for the predicted actions to be pre-loaded into cache before the user signals their intent, thereby reducing the latency associated with data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is retrieved from backend on demand when user performs action, then data retrieval accuracy is ensured, but latency is increased
Solution Approach 1:
The system performs preliminary actions by predicting user actions before they occur and pre-loading the required data into cache. The machine learning model analyzes user behavior patterns to anticipate next actions, and the system proactively retrieves and stores relevant data in advance, so that when the user actually performs the action, the data is already ready and latency is minimized.
2Loss of time
If data is pre-loaded into cache before user action, then latency is reduced, but cache memory usage increases
Solution Approach 1:
Instead of pre-loading all possible data or using excessive cache memory, the system applies partial action by selectively pre-loading only the data corresponding to predicted user actions. The machine learning model predicts which actions are most likely to occur, and the system pre-loads only that subset of data, achieving latency reduction while maintaining efficient cache utilization.
3Productivity
If machine learning model predicts user actions, then data retrieval efficiency is improved, but computational overhead increases
Solution Approach 1:
The machine learning model is trained to recognize and predict user behavior patterns, enabling the system to serve itself by automatically anticipating user actions and initiating data pre-loading without manual intervention. Once trained, the model efficiently processes user behavior data and generates predictions that trigger the pre-loading mechanism, making the system self-optimizing while maintaining reasonable computational overhead.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for providing predictive interface generation. As a user interacts with a user interface, information relating to the user, including the user activity within the user interface and/or historical data relating to the user is provided to a machine learning model. The machine learning model uses artificial intelligence and the received user data to predict a likely next action that the user will take. In some instances, multiple next actions are determined, each with a corresponding confidence score. A most likely subset of those results are then provided to a preloading system that retrieves the data from a database that is needed to provide the response to those actions. This data is then preloaded in memory for immediate access, in the event that one of the predicted actions is actually carried out by the user.