App Launch Tab Selection Using Intent Scores for Navigation
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Solution Overview
Problem
Users often struggle to navigate software applications efficiently, missing out on features and content due to unawareness of available pages and navigation options, leading to suboptimal user experience and missed updates.
Innovation Solution
A computing device uses a machine learning model to generate intent scores based on user interaction data, determining optimal navigation settings to directly open relevant pages upon application launch, thereby enhancing user engagement and content discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the application opens to a default page (e.g., application store page), then the application structure is simple and easy to implement, but users may miss relevant content and features they are interested in
Solution Approach 1:
The system performs preliminary analysis of user interaction data before the application launch to predict which page the user is most likely to access. This pre-computed intent score allows the application to proactively open to the predicted page, improving navigation efficiency without requiring complex real-time decision-making structures during application execution.
Solution Approach 2:
The application automatically analyzes its own usage data and self-adjusts its startup behavior by determining which page to open based on collected interaction patterns. This self-service approach eliminates the need for manual configuration or complex external control systems, resolving the contradiction between operational efficiency and system complexity.
2Loss of information
If the application tracks detailed usage information to determine user intent, then content discovery is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential features from detailed usage data that are most predictive of user intent, such as time spent on specific pages and frequency of access. By focusing on these key indicators rather than processing all available data, the system maintains high intent detection accuracy while reducing processing complexity and resource requirements.
Solution Approach 2:
The system transforms detailed usage data into a simplified intent score parameter that captures user preference in a single metric. This parameter transformation reduces the dimensionality of the data processing task, allowing the system to maintain accurate user intent detection without the computational burden of analyzing raw detailed data directly.
3Ease of operation
If the application opens to the last accessed page, then navigation simplicity is maintained, but user interest in other relevant content may be missed
Solution Approach 1:
The system dynamically adjusts the startup page selection based on current user behavior patterns rather than relying on static rules like always opening to the last accessed page. This dynamic approach allows the application to adapt to changing user interests while maintaining operational simplicity through automated decision-making based on real-time data analysis.
Data Source
AI summary
A computing device is configured to obtain information for an application. The computing device is further configured to generate, using a machine learning model and based on the usage information, at least one intent score. The computing device is further configured to determine, based on the at least one intent score, one or more navigation settings for the application, wherein the one or more navigation settings indicate a particular page that the application should open upon launching of the application. The computing device is further configured to cause, upon launching of the application, the application to open the particular page.