Content Selection via Navigational Performance Adjustment
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Solution Overview
Problem
Existing content management systems struggle to effectively select and rank content items based on presentation context, leading to suboptimal user engagement and relevance in search results pages.
Innovation Solution
The method involves obtaining performance data for content items in both navigational and non-navigational contexts, calculating a navigational performance adjustment factor for each content item slot, and adjusting predicted performance measures to prioritize content items based on their likelihood of being navigational, thereby enhancing user relevance and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content items are selected based on traditional ranking methods without considering presentation context, then the system complexity is low, but the user engagement and relevance are suboptimal
Solution Approach 1:
The patent segments the presentation context into distinct types (navigational vs. non-navigational) and applies different performance measures to each segment. This allows the system to handle complexity in a structured way while improving user engagement through context-appropriate content selection.
Solution Approach 2:
The system dynamically adjusts the content selection process by determining whether each presentation context is navigational or non-navigational and applying different performance measures accordingly. This dynamic adaptation improves user engagement without requiring complete system redesign.
2Measurement precision
If the system uses a single performance measure for all content items, then the implementation is simple, but the accuracy of content selection is insufficient
Solution Approach 1:
The patent applies different performance measures to different local contexts: navigational content items use one performance measure while non-navigational content items use another. This localized approach improves content selection accuracy without requiring complete system complexity.
Solution Approach 2:
The system changes the performance measure parameter based on the presentation context type. By switching between different performance measures (first performance measure for navigational, second for non-navigational), the system achieves higher accuracy without permanent complexity increase.
3Ease of operation
If the system adjusts predicted performance measures based on navigational context, then the user satisfaction increases, but the computational overhead increases
Solution Approach 1:
The system performs preliminary determination of whether each presentation context is navigational or non-navigational before selecting content items. This advance classification allows for efficient adjustment of performance measures without excessive computational overhead during the actual content selection process.
Data Source
AI summary
Methods, systems, and apparatus for selecting content items based on presentation context are presented. In one aspect, a method includes for each of a plurality of content item slots of a resource: obtaining first performance data based on a performance of content items that were presented in a navigational context in the content item slot; obtaining second performance data based on a performance of content items that were presented in a non-navigational context in the content item slot; determining, from the first and second performance data, a navigational performance adjustment factor for the content item slot, the navigational performance adjustment factor indicating, for the content item slot, a performance of content items presented in the navigational context relative to a performance of content items presented in the non-navigational context.