Digital Content Recommendation Ranking Algorithm
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Solution Overview
Problem
Existing recommendation systems face challenges in balancing the need to display personalized content relevant to users while ensuring a certain number of displays for native content sources, which may be less relevant, without negatively affecting user satisfaction.
Innovation Solution
A method and system that select candidate content items from both native and non-native sources, determining relevancy and completion parameters based on user interest profiles and display features, and ranking them using algorithms to generate digital content recommendations that balance relevance and display requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the recommendation system prioritizes displaying personalized content relevant to users, then user satisfaction is improved, but the display requirements of native sources may not be met
Solution Approach 1:
The system dynamically adjusts the ranking score by introducing a completion parameter that modifies the relevance-based ranking. This parameter change allows the system to balance between user satisfaction (relevance) and native source display requirements, resolving the contradiction by transforming the ranking mechanism from purely relevance-based to a hybrid model that incorporates display completion metrics
Solution Approach 2:
The system implements a feedback loop where the completion status of native source displays is continuously monitored and fed back into the ranking algorithm. This feedback mechanism allows the system to adjust recommendations in real-time to ensure native sources meet their display targets while maintaining overall recommendation quality and user satisfaction
2Productivity
If the recommendation system ensures display requirements for native sources, then native content visibility is improved, but relevance to users may deteriorate
Solution Approach 1:
The system modifies the ranking parameters by incorporating a completion parameter that works multiplicatively with the relevance score. This parameter adjustment ensures that native content display requirements are integrated into the ranking process without completely overriding relevance considerations, thus maintaining a balance between both objectives
Solution Approach 2:
The ranking algorithm dynamically adjusts the influence of the completion parameter based on current system state and content characteristics. This dynamic approach allows the system to flexibly balance between native source display requirements and content relevance, adapting to different scenarios to prevent deterioration of recommendation quality
3Manufacturing precision
If the system ranks content based solely on relevancy parameters, then content quality is improved, but native source display targets are not achieved
Solution Approach 1:
The system transforms the single-parameter relevance ranking into a multi-parameter ranking system that includes both relevance parameters and completion parameters. This parameter expansion allows the system to maintain content quality standards while simultaneously achieving native source display targets through the combined scoring mechanism
Data Source
AI summary
A method and a system for generating a digital content recommendation. The method comprises receiving a request for the digital content recommendation. Based on the request, a first content item and a second content item responsive to the request are selected, and a relevancy parameter and a completion parameter for each of the first content item and the second content item are determined. Based on the relevancy parameter and the completion parameter, the first content item and the second content item are ranked, and a digital content recommendation is generated based on the ranking of the first content item and the second content item.