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RecGPT Technical Report
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RecGPT Technical Report
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Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most current industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users' evolving and latent interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the whole recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction, merchants and the platform gain greater exposure and conversions. These comprehensive improvement results across all stakeholders validates that LLM-driven, intent-centric design can foster a more sustainable and mutually beneficial recommendation ecosystem.
Forward citations
Cited by 16 Pith papers
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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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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 seman...
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Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal 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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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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LLM-Based Generative Retrieval for Snapchat Content Recommendation
SnapLGR, a production LLM-based generative retrieval system for Snapchat short video, lifted View Time 0.37% and related engagement metrics in a 7-day A/B test, with offline ablation attributing most of the gain to de...
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When Language Models Meet NeuroGraphs: Exploring Enhanced Agentic LLM Framework Towards Brain Network Analysis
BrainAgent, a training-free agentic LLM framework with graph understanding, knowledge retrieval, case retrieval, and reflection, claims improved but still moderate connectome classification and interpretability.
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ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
ShopX is a single foundation model combining intent understanding, planning, and SID-native item fulfillment for agentic shopping, with claimed improvements over tool-mediated systems on Taobao logs.
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UniVA: Unified Value Alignment for Generative Recommendation in Online Advertising at Tencent
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 ...
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TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation
TriAlignGR integrates visual content and latent user interests into Semantic IDs via cross-modal alignment, CoT-based interest mining, and triangular multitask training to address content degradation and semantic opac...
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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.
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