arXiv:2602.10445v3 Announce Type: replace-cross Abstract: Generative Recommendation (GR) has excelled by framing recommendation as next-token prediction. This paradigm relies on Semantic IDs (SIDs) to tokenize large-scale items into discrete sequences. Existing GR approaches predominantly generate SIDs via Residual Quantization (RQ), where items are encoded into embeddings and then quantized to discrete SIDs. However, this paradigm suffers from inherent limitations: 1) Objective misalignment and semantic degradation stemming from the two-stage compression; 2) Error accumulation inherent in the
The continuous evolution of AI in recommendation systems, particularly the push towards more efficient and semantically aligned generative methods, necessitates addressing current limitations.
Improving semantic ID generation directly impacts the efficacy and scalability of generative AI recommendation systems, a core component of digital commerce and content platforms.
New methods for semantic ID generation promise to enhance the accuracy and relevance of AI-driven recommendations by overcoming existing limitations in current two-stage compression techniques.
- · E-commerce platforms
- · Content recommendation services
- · Adtech companies
- · AI research and development
- · Inefficient two-stage recommendation systems
- · Companies reliant on outdated recommendation paradigms
More accurate and personalized advertisement and content recommendations will become standard, leading to higher engagement rates.
The improved efficiency of product discovery could fuel growth in various digital markets, altering consumer purchasing patterns.
Enhanced generative recommendation quality might accelerate the 'enshittification' of the internet by creating more effective, yet potentially manipulative, algorithmic funnels.
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Read at arXiv cs.LG