ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
A survey of generative search and recommendation in the era of large language models
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TriAlignGR proposes a triangular multitask alignment framework with cross-modal semantic alignment, deep interest mining via chain-of-thought, and joint training on eight tasks to address content degradation and semantic opacity in Semantic ID-based generative recommendation.
A new framework integrating deep interest mining, cross-modal semantic alignment, and quality-aware reinforcement learning generates higher-quality Semantic IDs and outperforms prior methods on recommendation benchmarks.
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.
BBDRec applies Brownian bridge diffusion to enable direct item-to-history transitions in sequential recommendation, outperforming prior diffusion and sequential baselines on public datasets.
CQ-SID semantic IDs and EG-GRPO RL improve generative retrieval hit rates up to 26.76% over RQ-VAE baselines and deliver +1.15% GMV in live e-commerce A/B tests.
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.
A2Gen models temporal user action sequences with context-aware attention and autoregressive generation to improve short video recommendation accuracy, showing gains in watch time and retention on large-scale tests.
citing papers explorer
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Conditional Memory Enhanced Item Representation for Generative Recommendation
ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
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TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation
TriAlignGR proposes a triangular multitask alignment framework with cross-modal semantic alignment, deep interest mining via chain-of-thought, and joint training on eight tasks to address content degradation and semantic opacity in Semantic ID-based generative recommendation.
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Deep Interest Mining with Cross-Modal Alignment for SemanticID Generation in Generative Recommendation
A new framework integrating deep interest mining, cross-modal semantic alignment, and quality-aware reinforcement learning generates higher-quality Semantic IDs and outperforms prior methods on recommendation benchmarks.
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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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Brownian Bridge Diffusion for Sequential Recommendation
BBDRec applies Brownian bridge diffusion to enable direct item-to-history transitions in sequential recommendation, outperforming prior diffusion and sequential baselines on public datasets.
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Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
CQ-SID semantic IDs and EG-GRPO RL improve generative retrieval hit rates up to 26.76% over RQ-VAE baselines and deliver +1.15% GMV in live e-commerce A/B tests.
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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.
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Action-Aware Generative Sequence Modeling for Short Video Recommendation
A2Gen models temporal user action sequences with context-aware attention and autoregressive generation to improve short video recommendation accuracy, showing gains in watch time and retention on large-scale tests.