Recommendation Content Routing for Personalized User Actions
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Solution Overview
Problem
Conventional recommendation systems provide the same additional content about a digital item to different users, failing to account for individual user interests and behaviors, leading to reduced user satisfaction and engagement, and making it difficult to manage network anomalies and demand spikes.
Innovation Solution
A method and server that direct different users on separate navigation paths based on their interests, using machine learning techniques for anomaly detection and real-time path control, and provide personalized content from trusted service providers within their social circle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the same additional content is provided to all users about a digital item, then the system operation is simple, but user satisfaction and engagement are reduced
Solution Approach 1:
The patent applies local quality by providing different additional content to different users based on their individual interests and behaviors. Instead of uniform content delivery, the system tailors the content presentation to each user's profile, thereby improving user satisfaction while managing complexity through automated personalization algorithms.
Solution Approach 2:
The system changes parameters such as user interests, behavior patterns, and content preferences to dynamically determine which additional content to present. By adjusting these parameters based on user data, the system delivers personalized content without requiring manual intervention for each user, thus improving satisfaction while controlling system complexity.
2Productivity
If personalized navigation paths are provided to different users, then user engagement is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic navigation paths that adapt to each user's interests and behaviors in real-time. The navigation structure is not static but dynamically generated based on user profiles, allowing the system to improve engagement while managing complexity through automated decision-making algorithms that select and present relevant content paths.
Solution Approach 2:
The system enables self-service by allowing user data and behavior patterns to automatically guide the creation of personalized navigation paths. The system serves itself by using its own collected user information to generate appropriate navigation routes, reducing the need for external configuration and managing complexity through self-organizing algorithms.
3Ease of operation
If conventional recommendation systems provide same content to all users, then system management is easier, but anomaly detection becomes difficult
Solution Approach 1:
The patent incorporates feedback mechanisms that monitor user responses to recommended content and use this information to detect anomalies. By continuously gathering feedback on user interactions with personalized content, the system can identify unusual patterns or anomalies while maintaining ease of management through automated monitoring and adjustment processes.
Data Source
AI summary
Methods and servers for displaying a digital item to users of a recommendation system are disclosed. The method comprises determining first and second recommendable contents that include the digital item for a first and a second user of a first and a second electronic devices respectively. The digital item is associated with a plurality of actions. The method comprises triggering display of at least some content from the first and second recommendable content including the digital item on the first and second electronic devices respectively. The digital item is associated with a first action triggerable by the first user on the first electronic device and a second action triggerable by the second user on the second electronic device. The first and second actions are selected among the plurality of actions based information about the first and second users respectively.