Action Interface Shortcut Prediction to Reduce Web Page Loads
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Solution Overview
Problem
Existing web search systems require multiple page loads to perform desired actions, leading to increased network bandwidth usage, processing cycles, and energy consumption, without providing a direct path to relevant web resources.
Innovation Solution
Implementing a machine-learned action prediction model that predicts a resource locator for a relevant action interface based on context data, generating a shortcut to directly load the desired action interface, thereby reducing the number of page loads and optimizing network and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional web search systems load multiple pages to perform desired actions, then users can access relevant web resources, but network bandwidth usage, processing cycles, and energy consumption increase
Solution Approach 1:
The system performs preliminary action by predicting the resource locator of the action interface before the user actually needs to access it. The machine-learned model analyzes context data (search query, browser history, current page state) to preemptively determine which action interface the user will need, allowing direct navigation without intermediate page loads.
Solution Approach 2:
The machine-learned action prediction model serves as an intermediary between the user's search intent and the target action interface. Instead of requiring users to manually navigate through multiple pages, the model acts as a mediator that translates context data into predicted resource locators, enabling direct access to the desired interface.
2Productivity
If traditional web search systems load multiple pages to perform desired actions, then users can access relevant web resources, but the number of network transmissions and page loads increases
Solution Approach 1:
The system performs preliminary prediction of the action interface resource locator based on context data before user navigation occurs. By analyzing search queries, browser history, and current page state in advance, the model prepares the predicted destination, eliminating the need for sequential page loads and reducing time loss.
Solution Approach 2:
The system skips intermediate pages by directly navigating to the predicted action interface. Instead of loading multiple pages in sequence, the machine-learned model enables the system to rush through the navigation process by jumping directly to the target interface based on predicted user intent.
3Ease of operation
If traditional web search systems load multiple pages to perform desired actions, then users can access relevant web resources, but processing cycles increase
Solution Approach 1:
The machine-learned action prediction model serves as an intermediary layer that handles the complexity of analyzing context data and predicting resource locators. This intermediary absorbs the computational complexity, allowing the rest of the system to maintain simplicity while gaining direct navigation capability.
Solution Approach 2:
The system performs self-service by automatically analyzing context data and predicting the action interface resource locator without user intervention. The machine-learned model autonomously processes search queries, browser history, and page state to determine the next destination, eliminating the need for manual navigation while managing system complexity internally.
Data Source
AI summary
In one example aspect, the present disclosure provides an example computer-implemented method for implementing a machine-learned action prediction model. The example method can include transmitting, by a computing system and to a search system, a search query for retrieving search results indicating web resources related to the search query. The example method can include receiving, by the computing system and from the search system, the search results. The example method can include determining, by the computing system and using a machine-learned action prediction model, based on context data associated with the search query, a resource locator of an action interface of a web resource associated with at least one search result. The example method can include generating, by the computing system, a shortcut to the action interface using the resource locator.