Location-Based Content Selection Using Historical Sequence Analysis
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Solution Overview
Problem
Current systems for selecting location-based content on mobile devices lack efficiency in determining user interaction likelihood, as they do not effectively utilize historical data to personalize content presentation based on user location and behavior.
Innovation Solution
A method and system that analyze a sequence of previously selected third-party content in conjunction with the current location of a mobile device to determine a likelihood score for interaction, using machine learning models to select and present relevant content, while ensuring user privacy through anonymization and control over data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If location-based content is selected based solely on current location, then content presentation is simplified, but user engagement and interaction likelihood are reduced
Solution Approach 1:
The system performs preliminary analysis of historical content sequences and location data to pre-determine likelihood scores before content presentation. By analyzing past behavior patterns in advance, the system prepares personalized content recommendations that are more likely to engage users, resolving the contradiction between simple presentation and high interaction likelihood.
Solution Approach 2:
The system incorporates feedback loops by continuously monitoring user interactions with previously presented content and adjusting future content selection accordingly. This feedback mechanism enables the system to learn from past performance and improve content relevance, increasing interaction likelihood while maintaining operational simplicity through automated learning.
2Reliability
If historical data is analyzed to personalize content, then user engagement is improved, but system complexity increases
Solution Approach 1:
The system segments the complex historical data into manageable components such as content sequences, location timestamps, and interaction metrics. By dividing the complex analysis task into discrete, processable segments, the system can efficiently personalize content without overwhelming computational complexity, thus improving engagement while controlling system complexity.
Solution Approach 2:
The system transforms complex historical data into simplified parameters like likelihood scores and engagement predictions. By changing the representation of complex data into more manageable parameter forms, the system reduces analysis complexity while maintaining the ability to personalize content effectively, resolving the contradiction between improved engagement and reduced complexity.
3Reliability
If multiple factors are considered for content selection, then content relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary computations of likelihood scores based on historical data and location patterns before actual content selection. By pre-processing and storing these scores, the system can quickly retrieve and present relevant content without performing complex analyses in real-time, thus improving content relevance while minimizing processing time during actual content delivery.
Data Source
AI summary
Systems and methods include retrieving data indicative of a sequence of content that were previously selected for presentation by a mobile device based in part on physical locations of the mobile device. The sequence of selected content may be used with the current location of the mobile device to determine a likelihood score for a piece of content. Based on the likelihood score, the piece of content may be selected and provided to the mobile device for presentation.