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Cross-Domain Semantic IDs

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Quantizing Intent: Cross-Domain Semantic IDs from Organic Activity for Industrial Ranking

arXiv:2606.01396v1 Announce Type: new Abstract: Ads click-through rate (CTR) prediction is constrained by sparse user supervision: most users engage with ads infrequently while generating dense behavioral evidence in organic surfaces such as feed. Transferring these cross-domain signals into ads ranking is difficult due to domain mismatch, serving cost, and production complexity. We introduce cross-domain user Semantic IDs (SIDs) derived from organic feed activity and show that behavioral...

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Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606.03866v1 Announce Type: new Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID space via post-training (e.g., SFT and RL) remains challenging. Existing LLM4Rec paradigms are bottlenecked by two main issues: (1) the difficulty of measuring and improving chain-of-thought (CoT) quality in open-domain recommendation during SFT, and (2) the neglect of...

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