Post
1655
Exciting breakthrough in Streaming Recommendation Systems!
@BytedanceTalk
researchers have developed "Long-Term Interest Clock" (LIC), a revolutionary approach to understand user preferences throughout the day.
>> Technical Innovation
The system introduces two groundbreaking modules:
- Clock-based General Search Unit (Clock-GSU): Intelligently retrieves relevant user behaviors by analyzing time patterns and content similarity
- Clock-based Exact Search Unit (Clock-ESU): Employs time-gap-aware attention mechanism to precisely model user interests
>> Key Advantages
LIC addresses critical limitations of existing systems by:
- Providing fine-grained time perception instead of discrete hour-based recommendations
- Analyzing long-term user behavior patterns rather than just short-term interactions
- Operating at item-level granularity versus broad category-level interests
>> Real-World Impact
Already deployed in Douyin Music App, the system has demonstrated remarkable results:
- 0.122% improvement in user active days
- Significant boost in engagement metrics including likes and play rates
- Enhanced user satisfaction with reduced dislike rates
>> Under the Hood
The system processes user behavior sequences spanning an entire year, utilizing multi-head attention mechanisms and sophisticated time-gap calculations to understand user preferences. It pre-computes embeddings stored in parameter servers for real-time performance, making it highly scalable for production environments.
This innovation marks a significant step forward in personalized content delivery, especially for streaming platforms where user preferences vary throughout the day. The research has been accepted for presentation at WWW '25, Sydney.
>> Technical Innovation
The system introduces two groundbreaking modules:
- Clock-based General Search Unit (Clock-GSU): Intelligently retrieves relevant user behaviors by analyzing time patterns and content similarity
- Clock-based Exact Search Unit (Clock-ESU): Employs time-gap-aware attention mechanism to precisely model user interests
>> Key Advantages
LIC addresses critical limitations of existing systems by:
- Providing fine-grained time perception instead of discrete hour-based recommendations
- Analyzing long-term user behavior patterns rather than just short-term interactions
- Operating at item-level granularity versus broad category-level interests
>> Real-World Impact
Already deployed in Douyin Music App, the system has demonstrated remarkable results:
- 0.122% improvement in user active days
- Significant boost in engagement metrics including likes and play rates
- Enhanced user satisfaction with reduced dislike rates
>> Under the Hood
The system processes user behavior sequences spanning an entire year, utilizing multi-head attention mechanisms and sophisticated time-gap calculations to understand user preferences. It pre-computes embeddings stored in parameter servers for real-time performance, making it highly scalable for production environments.
This innovation marks a significant step forward in personalized content delivery, especially for streaming platforms where user preferences vary throughout the day. The research has been accepted for presentation at WWW '25, Sydney.