Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling
arXiv:2606.07546v1 Announce Type: cross Abstract: Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by two bottlenecks: the semantic sparsity of atomic Video IDs and the quadratic computational complexity of Transformers. Traditional orthogonal Video IDs fail to capture content relationships and demand large embedding tables, while the quadratic complexity of self-attention restricts the maximum sequence length under strict industrial latency and resource constraints. In this work, we present a prod
The proliferation of short-form video content and the increasing sophistication of AI models necessitate more efficient and scalable recommendation systems to keep pace with user demand and computational constraints.
Improving user engagement and content discovery in vast short-form video platforms has direct implications for advertising revenue, content creation, and platform dominance, impacting the digital economy.
This research introduces methods to overcome key limitations in short-form video recommendation, potentially leading to more accurate and real-time user experiences while reducing computational overhead for platforms.
- · Short-form video platforms
- · AI researchers and developers
- · Content creators
- · Users
- · Legacy recommendation systems
- · Inefficient AI architectures
More relevant and engaging content feeds for users of platforms like TikTok and YouTube Shorts.
Increased user retention and monetization potential for platforms due to enhanced recommendation quality.
The development of new content formats and user interaction patterns enabled by highly responsive and contextually aware AI recommendation engines.
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Read at arXiv cs.LG