Live Stream Distribution Using Idle Scores for Active Recommendations
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Solution Overview
Problem
Existing live streaming platforms struggle to effectively recommend streams that enhance viewer engagement and interaction, often recommending inactive or unengaging content to users.
Innovation Solution
A system and method for determining an idle score of a distributor based on their activity level, calculating a priority score for recommending streams, and customizing recommendations to individual viewer preferences using idle and interaction scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system recommends live streams based on traditional metrics (view count, follower count), then popular streams are promoted, but inactive or unengaging content may be recommended to viewers
Solution Approach 1:
The patent introduces a fundamentally new parameter (idle score) to measure distributor activity, changing the basis of recommendation from static metrics like follower count to dynamic metrics that capture real-time engagement levels. This parameter change enables the system to distinguish between popular but inactive streams and actively engaging streams, improving recommendation accuracy without requiring complex multi-factor algorithms
Solution Approach 2:
The patent replaces traditional recommendation mechanisms based on social metrics (followers, view counts) with a new mechanism based on activity-based scoring. This substitution shifts the foundation of the recommendation system from popularity-based to engagement-based, allowing it to reliably identify and promote streams with active distributors
2Measurement precision
If the system monitors distributor activity in real-time to calculate idle scores, then recommendation quality improves, but system resource consumption increases
Solution Approach 1:
The patent implements partial monitoring by focusing only on key activity indicators necessary for calculating the idle score, rather than comprehensively analyzing all distributor actions. This selective approach maintains measurement precision for the critical metric (distributor activity level) while avoiding the computational overhead of monitoring every system event, thus reducing overall resource consumption
Solution Approach 2:
The system leverages existing activity data that is already being collected for other purposes (stream metadata, viewer interactions) and repurposes it for idle score calculation. By using data that would otherwise be processed or stored anyway, the system achieves precise activity measurement without significant additional computational burden
Data Source
AI summary
The present disclosure relates to a system and a method for stream distribution. The method includes: determining an idle score of a distributor in a live stream of the distributor; and determining a priority score of recommending the live stream to a first viewer according to the idle score of the distributor, wherein the idle score increases as the distributor is less active in the live stream.