A single LLM trained to emit semantic item codes can fulfill complex shopping intents with fewer tool hand-offs, improving multi-turn follow-up on Taobao-derived tasks.
Recgpt technical report
8 Pith papers cite this work. Polarity classification is still indexing.
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cs.IR 8years
2026 8roles
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RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
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.
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.
Fine-tuned LLM acts as ancillary advertiser predictor in production ads RecSys, augmenting retrieval and ranking with measurable offline and online gains.
RecGPT-Mobile runs a compact LLM on phones to understand evolving user intent from behaviors and improve mobile e-commerce recommendations.
citing papers explorer
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ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
A single LLM trained to emit semantic item codes can fulfill complex shopping intents with fewer tool hand-offs, improving multi-turn follow-up on Taobao-derived tasks.
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RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
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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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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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Fine-Tuned LLM as a Complementary Predictor Improving Ads System
Fine-tuned LLM acts as ancillary advertiser predictor in production ads RecSys, augmenting retrieval and ranking with measurable offline and online gains.
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RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation
RecGPT-Mobile runs a compact LLM on phones to understand evolving user intent from behaviors and improve mobile e-commerce recommendations.