SIDInspector provides a standardized adapter contract and mapping-level probes for Semantic-ID tokenizers, with empirical contrasts showing high aliasing in GRID-style exports and superior prefix alignment from deterministic controls on Musical items.
A survey of generative search and recommendation in the era of large language models
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ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
Injecting commercial value into Semantic ID construction, autoregressive decoding, and online beam search improves generative advertising recommendation, with reported offline HR@100 +37.04% and online GMV +1.5%.
Adding visual evidence and LLM-mined item-side intent descriptors before semantic-ID quantization, plus a relevance-gated quality reward, improves SID-based generative recommendation on three Amazon categories.
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.
TriAlignGR introduces cross-modal alignment, deep interest mining via CoT, and triangular multitask training to fix semantic degradation and opacity in SID-based generative recommendation.
A2Gen treats timed user actions on short videos as generative sequences and reports large-scale online gains in watch time, interactions, and retention.
A workshop proposal to address efficiency bottlenecks in using large multimodal foundation models for information retrieval tasks.
citing papers explorer
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SIDInspector: A Mapping-First Diagnostic Resource for Semantic-ID Tokenizers
SIDInspector provides a standardized adapter contract and mapping-level probes for Semantic-ID tokenizers, with empirical contrasts showing high aliasing in GRID-style exports and superior prefix alignment from deterministic controls on Musical items.
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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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UniVA: Unified Value Alignment for Generative Recommendation in Online Advertising at Tencent
Injecting commercial value into Semantic ID construction, autoregressive decoding, and online beam search improves generative advertising recommendation, with reported offline HR@100 +37.04% and online GMV +1.5%.
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Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation
Adding visual evidence and LLM-mined item-side intent descriptors before semantic-ID quantization, plus a relevance-gated quality reward, improves SID-based generative recommendation on three Amazon categories.
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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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TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation
TriAlignGR introduces cross-modal alignment, deep interest mining via CoT, and triangular multitask training to fix semantic degradation and opacity in SID-based generative recommendation.
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Action-Aware Generative Sequence Modeling for Short Video Recommendation
A2Gen treats timed user actions on short videos as generative sequences and reports large-scale online gains in watch time, interactions, and retention.
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The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
A workshop proposal to address efficiency bottlenecks in using large multimodal foundation models for information retrieval tasks.