Media Notification Targeting via Interest Metrics
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Solution Overview
Problem
Content sharing platforms face challenges in attracting users' attention to media content items outside their current subscriptions without triggering adverse reactions to unsolicited notifications.
Innovation Solution
A method where a media content sharing platform identifies target users and media content items with high user interest metrics, such as channel affinity scores and click-through rates, to selectively provide notifications, throttling frequency to minimize user adverse reactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the platform sends notifications to users about media content items outside their subscriptions, then user attention to relevant content is improved, but user adverse reactions increase due to excessive unsolicited notifications
Solution Approach 1:
The notification system applies local quality by customizing notifications for each user based on their specific interests, consumption patterns, and engagement history. Instead of sending uniform notifications to all users, the system tailors the content, timing, and frequency of notifications to match individual user preferences and behaviors, thereby improving relevance while reducing adverse reactions.
Solution Approach 2:
The system dynamically adjusts notification parameters such as frequency, timing, and content based on user engagement metrics and interest levels. By monitoring user interactions and modifying notification parameters in real-time, the platform optimizes the balance between delivering relevant content and avoiding excessive unsolicited notifications that cause user fatigue or adverse reactions.
2Illumination intensity
If the platform increases notification frequency to attract user attention, then content visibility is improved, but user overload increases leading to adverse reactions
Solution Approach 1:
The notification system implements periodic action by sending notifications at strategically determined intervals based on user engagement patterns and content relevance. Instead of continuous or frequent notifications, the system uses timing algorithms to space out notifications optimally, ensuring content visibility while preventing user overload and adverse reactions from excessive notification volume.
3Measurement precision
If the platform sends targeted notifications based on user interest metrics, then notification relevance is improved, but system complexity increases due to metric calculation and user profiling
Solution Approach 1:
The notification system applies self-service by automatically collecting user interaction data, calculating interest metrics, and generating personalized notifications without requiring manual intervention. The system autonomously profiles users based on their consumption patterns and engagement history, then uses these profiles to generate and send targeted notifications, thereby achieving high measurement precision while managing system complexity through automation.
Data Source
AI summary
Implementations of the disclosure describe systems and methods for triggering user notifications of media content items. It is determined that a plurality of media content items has a value of an interest metric exceeding a defined threshold value. The plurality of media content items are represented by a list of media content items compiled based on a pre-defined criterion. The interest metric reflects interest of a user to the plurality of media content items. The plurality of media content items is provided by a content channel that has not been subscribed to by the user. Among the plurality of media content items, a media content item that has not been consumed by the user is selected. A notification is provided to a device employed by the user to notify the user of the media content item.