New Framework Improves Short-Form Video Recommendations by Processing Longer User Watch Histories
Researchers have developed a production-deployed system that can process much longer user watch histories for short-form video recommendations at billion-user scale. The approach addresses two key technical challenges: replacing traditional video IDs with semantic-based representations and introducing a more efficient transformer architecture that reduces computational complexity. This advancement enables recommendation systems to consider more user behavior data while maintaining the speed required for real-time deployment.
A new framework presented on arXiv tackles fundamental scalability challenges in short-form video recommendation systems. Traditional approaches use orthogonal video IDs that fail to capture content relationships and require massive embedding tables, while the quadratic computational complexity of transformer self-attention limits how much user history can be processed within production constraints. The researchers address the representation bottleneck by adopting semantic-native IDs that compress embedding table sizes and generalize better to new content, and introduce a Global-Aware Compression Transformer using temporal folding and unified global query integration to reduce memory and computational overhead. Offline testing showed order-of-magnitude improvements in memory efficiency and computational cost, enabling longer sequence modeling. The system has been deployed in production and demonstrated measurable improvements in user engagement and content consumption through large-scale A/B testing.
What's missing
The paper does not specify which company deployed this system or provide details on the specific scale of the A/B tests (number of users, duration, or exact engagement metrics). The limitations and failure cases of the semantic ID approach are not discussed in the abstract.
What different sources said
- arXiv cs.AICenter
Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling
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