arXiv:2509.25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals. One popular modern approach is to use semantic IDs (SIDs), which are discrete codes quantized from the embeddings of modality encoders (e.g., large language or vision models), to represent items in an autoregressive user interaction sequence modeling setup (henceforth, SID-based GR). While generative model
The proliferation of advanced generative models, particularly large language models, has enabled new approaches to data representation and sequence modeling in recommendation systems.
This research suggests a more robust and unified approach to recommender systems by integrating rich semantic understanding with collaborative filtering, potentially leading to more accurate and nuanced recommendations.
The use of semantic IDs for item representation and autoregressive modeling could enable more sophisticated personalized recommendations that better capture user intent and item attributes.
- · E-commerce platforms
- · Content streaming services
- · Advertising technology companies
- · Generative AI model developers
- · Legacy recommender system providers
- · Companies with limited item metadata
- · Platforms unable to integrate complex AI models
More accurate and personalized recommendations will improve user engagement and conversion rates.
Enhanced recommendation quality could consolidate market share for platforms that adopt these advanced techniques effectively.
The ability to generate recommendations from latent semantic space could lead to entirely new forms of content discovery and product development.
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Read at arXiv cs.AI