Live-Stream Recommendation Prioritization for New Streamer Exposure
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Solution Overview
Problem
New streamers in live-streaming platforms often struggle to attract viewers quickly, leading to a high likelihood of abandoning their streams, while conventional systems favor popular streams over quality, creating an uneven playing field.
Innovation Solution
A server mechanism that prioritizes recommending new streams by specifying them for preferential display on user terminals based on streamer level, similarity scores, and user pools, ensuring they appear prominently among a limited number of active users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system recommends live-streams based on popularity and viewer counts, then popular streamers can quickly attract more viewers, but new streamers struggle to gain initial viewers and may give up live-streaming
Solution Approach 1:
The system performs preliminary action by proactively recommending new live-streams to users before they can naturally accumulate viewers. The recommendation unit identifies new streamers and actively pushes their live-stream information to user terminals, creating initial viewership opportunities before the streamers would otherwise fail to attract any audience.
2Reliability
If the system actively promotes new streamers to help them attract viewers, then new streamers can maintain motivation and continue live-streaming, but the system complexity increases due to additional recommendation mechanisms
Solution Approach 1:
The system applies local quality by creating a specialized recommendation path specifically for new streamers, rather than applying uniform recommendation rules to all streamers. The recommendation unit distinguishes between new and established streamers, applying different recommendation strategies tailored to each group's specific needs and characteristics.
3Adaptability or versatility
If the system recommends a large number of live-streams to users, then users have more choices and new streamers get more exposure, but the information overload increases and user experience deteriorates
Solution Approach 1:
The system applies partial action by selectively recommending only a subset of new live-streams to each user, rather than presenting all available new streams. The recommendation unit controls the quantity and quality of recommendations to maintain user experience while still providing adequate exposure opportunities for new streamers.
Data Source
AI summary
A server includes: a specifying unit for specifying a live-stream to be preferentially recommended; an obtaining unit for obtaining, for the specified live-stream to be preferentially recommended, a number of users to whom the live-stream is recommended; a generating unit for generating a group of users by selecting a number of users corresponding to the obtained number of users to whom the live-stream is recommended; and a setting unit for setting a list of live-streams to be recommended to a user included in the generated group of users such that the live-stream to be preferentially recommended is presented in priority over other live-streams in the list on a terminal of the user.