RAGR builds mixed item-review sequences for generative recommendation and uses DPO alignment to favor item tokens, reporting gains over prior GR baselines on three datasets.
Nezha: A zero-sacrifice and hyperspeed decoding architecture for generative recommendations
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.IR 4years
2026 4representative citing papers
UniSID jointly optimizes embeddings and Semantic IDs end-to-end with multi-granularity contrastive learning and summary-based reconstruction, outperforming RQ-based methods by up to 4.62% in Hit Rate for ad recommendation.
UniVA unifies value alignment in generative recommendation via a Commercial SID tokenizer, eCPM-aware RL decoder, and personalized beam search, reporting 37% offline Hit Rate gains and 1.5% online GMV lift on Tencent WeChat Channels.
citing papers explorer
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RAGR: Review-Augmented Generative Recommendation
RAGR builds mixed item-review sequences for generative recommendation and uses DPO alignment to favor item tokens, reporting gains over prior GR baselines on three datasets.
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End-to-End Semantic ID Generation for Generative Advertisement Recommendation
UniSID jointly optimizes embeddings and Semantic IDs end-to-end with multi-granularity contrastive learning and summary-based reconstruction, outperforming RQ-based methods by up to 4.62% in Hit Rate for ad recommendation.
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Unified Value Alignment for Generative Recommendation in Industrial Advertising
UniVA unifies value alignment in generative recommendation via a Commercial SID tokenizer, eCPM-aware RL decoder, and personalized beam search, reporting 37% offline Hit Rate gains and 1.5% online GMV lift on Tencent WeChat Channels.
- Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation