arXiv:2512.24787v3 Announce Type: replace-cross Abstract: Slate recommendation, which presents users with a ranked item list in a single display, is ubiquitous across mainstream online platforms. While recent generative recommendation methods have shown strong potential in modeling item sequences with semantic IDs, directly applying them to industrial-scale slate recommendation faces a fundamental disconnect: entangled SID spaces confound high-level list planning, fine-grained autoregressive decoding over long sequences limits semantic planning efficiency, and token-level objectives misalign w
The continuous evolution of AI and deep learning research is leading to more sophisticated recommendation systems at industrial scale, addressing past limitations. This is a natural progression as generative AI matures and seeks broader applications.
Advanced generative recommendation frameworks improve user engagement and monetization for large online platforms, directly impacting their core business models and competitive advantage. It signifies a move towards more intelligent and personalized user experiences.
Recommendation systems for online platforms will become more efficient and capable of handling complex item hierarchies, reducing the disconnect between generative AI models and industrial-scale deployment challenges.
- · Tencent
- · Large online platforms
- · Generative AI developers
- · E-commerce platforms
- · Platforms with less advanced recommendation systems
- · Legacy recommendation system providers
Improved user experience and increased revenue for platforms implementing such generative recommendation systems.
Accelerated adoption of generative AI in other areas requiring complex sequence modeling and personalized content delivery.
Enhanced data moats and competitive advantages for companies that can effectively deploy and scale these advanced AI systems.
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Read at arXiv cs.AI