Digital Asset Optimization via Category Transitions and Serendipity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting user interest in digital content, particularly branded content, have shown limited success as they primarily focus on user interests without effectively incorporating category transitions and serendipity in recommendation algorithms.
Innovation Solution
A system that organizes digital assets in a hierarchical taxonomy, using a directed graph to measure similarity between categories, combines observed historical data with randomized values to encourage serendipity, and optimizes the presentation of targeted assets based on historical yields and follow-on rates, ensuring efficient consumption of branded content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If branded content is presented at random in conjunction with viewed content, then content delivery is simple, but user engagement and prediction accuracy are poor
Solution Approach 1:
The patent segments the recommendation system into multiple independent components: user interest modeling, category transition analysis, serendipity injection, and optimization module. Each component processes specific aspects of content recommendation independently, allowing complex predictions to be built from simpler modular units that can be developed and maintained separately.
Solution Approach 2:
The system performs preliminary actions by pre-computing user interest profiles, category transition probabilities, and content similarity metrics before actual recommendation needs arise. This advance preparation enables fast, accurate recommendations without real-time complex calculations, improving prediction reliability while managing system complexity.
2Reliability
If recommendation algorithms focus only on user interests, then implementation is straightforward, but prediction success is limited
Solution Approach 1:
The patent merges three distinct analytical approaches: user interest analysis, category transition modeling, and serendipity factors. These previously separate methods are integrated into a unified recommendation framework where each component contributes to the final prediction, improving overall prediction success by capturing multiple dimensions of user behavior simultaneously.
Solution Approach 2:
The recommendation algorithm uses composite modeling by combining multiple data sources and prediction methods into a unified model. Just as composite materials combine different substances to achieve superior properties, the algorithm combines user interest data, category transition patterns, and serendipity metrics to achieve prediction accuracy superior to any single method alone.
3Productivity
If targeted assets are optimized based on historical yields and follow-on rates, then content consumption efficiency improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary computation of historical yield rates and follow-on rates during off-peak periods or in batch processing mode. By pre-calculating these metrics before they are needed for real-time optimization, the system improves content consumption efficiency during peak usage while avoiding the energy cost of real-time computation.
Solution Approach 2:
The optimization system uses historical data to automatically train and refine its own prediction models without requiring continuous external computational resources. The system serves itself by leveraging its accumulated historical data to improve future predictions, reducing ongoing computational energy requirements while maintaining high productivity.
Data Source
AI summary
Branded content, or a target asset, may be included in a set of ordered assets based on the category of an anchor asset. Fill rates, total views of the target asset, or a combination may be used in selecting an optimization strategy. A dual optimization may be used to reduce the burden of presentation based on historical yield rates and follow-on rates observed from category transition data. Serendipity may be incorporated in the process through use of a reserve pool of transitions.