Connectivity Analyzer Access Vector Content Selection
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Solution Overview
Problem
Content sharing services face challenges in determining and serving relevant content to users based on their connectivity aspects, such as device type, connection speed, time of access, and day, which affects user engagement and revenue generation.
Innovation Solution
A system and method that includes a connectivity analyzer to create an access vector based on detected connectivity aspects, such as device type, connection speed, time, and day, to retrieve and serve related content, thereby enhancing user engagement and revenue generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content is selected based on visitor's perceived interest and previous accesses, then visitor engagement is improved, but the system cannot adapt to varying connectivity conditions (device type, connection speed, time, day)
Solution Approach 1:
The system segments connectivity information into distinct aspects (device type, connection speed, time of access, day of access) and processes each aspect separately through the connectivity analyzer to generate an access vector. This segmentation allows the system to handle complex connectivity variations without overwhelming complexity by breaking down the adaptation problem into manageable components.
Solution Approach 2:
The connectivity analyzer performs preliminary analysis of connectivity aspects before content selection occurs. By pre-processing connectivity information and creating an access vector that encapsulates connectivity characteristics, the system prepares adaptation data in advance, enabling faster and more efficient content selection that considers both visitor interest and connectivity conditions.
2Productivity
If more shared content is served to increase revenue, then revenue is improved, but content relevance to specific connectivity conditions may deteriorate
Solution Approach 1:
The system dynamically adjusts content selection based on real-time connectivity conditions captured in the access vector. Rather than serving static content or uniformly increasing shared content volume, the connectivity-aware content selection adapts the type, format, and amount of content served to match current connectivity conditions, thereby maintaining relevance while optimizing revenue opportunities.
Solution Approach 2:
The system changes selection parameters based on connectivity aspects analyzed in the access vector. Different connectivity conditions (e.g., mobile vs. desktop, fast vs. slow connection, different times of day) trigger different content selection parameters, ensuring that content relevance is maintained across varying conditions while still serving multiple shared content items to maximize revenue.
3Measurement precision
If connectivity analysis is performed to improve content personalization, then content relevance is improved, but processing time and system complexity increase
Solution Approach 1:
The connectivity analyzer extracts only the essential connectivity aspects (device type, connection speed, time, day) needed for content personalization from the full set of available data. By taking out only the most relevant connectivity information and excluding unnecessary details, the system achieves effective personalization while minimizing processing time and computational overhead.
Solution Approach 2:
The system performs partial connectivity analysis by focusing on the four key aspects (device type, connection speed, time, day) rather than analyzing all possible visitor and connectivity parameters. This partial action approach provides sufficient personalization precision for effective content selection without the excessive processing time that would result from comprehensive analysis of all available data.
Data Source
AI summary
A system and method for determining related content to serve based on connectivity is provided. The system includes a connectivity analyzer to analyze an aspect of the connectivity of the detected access to the content sharing service; a vector aggregator to create an access vector based on the analyzed aspect of the connectivity; and a related content retriever to retrieve related content based on the access vector.