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From the 1 of 8 linked papers with an AI index.

most citedFORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets

1 citations · 1 across the 4 of their papers we have counts for

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cs.IR2026

RecGPT-V3 Technical Report

Bowen Zheng, Chao Yi, Dian Chen +26

Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecG…

cs.IR2026

ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping

Jiacheng Chen, Tao Zhang, Manxi Lin +26

ShopX is a foundation model that directly translates user shopping intents into item-space actions using semantic IDs, integrating intent understanding, planning, and item retrieva…

cs.IR20261 cited

FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets

Kairui Fu, Tao Zhang, Shuwen Xiao +9

Semantic identifiers (SIDs) have gained increasing attention in generative retrieval (GR) for recommendation due to their meaningful semantic discriminability. However, current stu…

cs.IR2026

RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation

Kairui Fu, Changfa Wu, Kun Yuan +8

Generative retrieval (GR) has emerged as a promising paradigm in recommendation systems by autoregressively decoding identifiers of target items. Despite its potential, current app…

cs.IR2026

CoNRec: Context-Discerning Negative Recommendation with LLMs

Xinda Chen, Jiawei Wu, Yishuang Liu +5

Understanding what users like is relatively straightforward; understanding what users dislike, however, remains a challenging and underexplored problem. Research into users' negati…

cs.IR2025

SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation

Yining Yao, Ziwei Li, Shuwen Xiao +5

In recommendation systems, predicting Click-Through Rate (CTR) is crucial for accurately matching users with items. To improve recommendation performance for cold-start and long-ta…