Content Prioritization via User Frequency Scoring
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Solution Overview
Problem
Existing content prioritization systems fail to optimally rank content items for users, as they do not adequately consider user frequency, leading to suboptimal display of recent versus important content, depending on the user's interaction frequency with the service.
Innovation Solution
A system that calculates a user frequency score based on request history and adjusts priority scores for content items using age and importance scores, prioritizing more recent content for frequent users and emphasizing importance for infrequent users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If content items are prioritized based on recency, then frequent users can access recent content, but important content may be overlooked for infrequent users
Solution Approach 1:
The patent applies dynamics by making the content prioritization strategy adaptive and flexible. The system dynamically adjusts prioritization based on user interaction frequency: frequent users receive recency-based prioritization while infrequent users receive importance-based prioritization. This dynamic adaptation resolves the contradiction by allowing the system to switch between competing objectives based on user behavior patterns.
Solution Approach 2:
The patent applies local quality by customizing content prioritization to specific user segments. Instead of using a uniform prioritization strategy for all users, the system identifies different user groups (frequent vs. infrequent users) and applies different prioritization rules to each group. This allows recent content to be prioritized for frequent users while important content is prioritized for infrequent users, resolving the contradiction through localized optimization.
2Reliability
If content items are prioritized based on importance, then important content is displayed, but recent content may be overlooked for frequent users
Solution Approach 1:
The system dynamically adjusts prioritization strategies based on user interaction frequency. For frequent users, the system applies recency-based prioritization to reduce time loss, while for infrequent users, it applies importance-based prioritization to ensure content reliability. This dynamic switching resolves the contradiction by adapting the prioritization objective to user behavior patterns.
Solution Approach 2:
The patent applies local quality by segmenting users into frequent and infrequent groups and applying different prioritization rules to each segment. This localized approach allows the system to optimize for recency among frequent users while optimizing for importance among infrequent users, thereby resolving the contradiction through differentiated service delivery.
3Device complexity
If a uniform prioritization strategy is used for all users, then system complexity is reduced, but user experience is suboptimal
Solution Approach 1:
The patent applies parameter changes by modifying the prioritization parameters based on user interaction frequency. The system changes the prioritization parameter (recency weight vs. importance weight) according to whether the user is frequent or infrequent. This parameter adaptation allows the system to maintain relatively simple architecture while achieving optimized user experience through data-driven parameter adjustment.
Solution Approach 2:
The system implements self-service by automatically identifying user interaction patterns and autonomously adjusting prioritization strategies without requiring manual configuration. The system monitors user behavior, classifies users into frequent or infrequent groups, and automatically applies appropriate prioritization rules, thereby reducing operational complexity while improving user experience through automated adaptation.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for prioritizing content items based on a request frequency for a user. A system is configured to receive a request for a user interface containing content items for a user, retrieve, in response to the request for the user interface containing content items for the user, a set of content items for the user, and calculate a request frequency score for the user based on a frequency of user requests for the user interface containing content items for the user. The system may further be configured to adjust a priority score for each content item in the set of content items based on the request frequency score for the user and provide the set of content items for display to the user based on the priority score for each content item.