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cs.IR2026

Native Multimodal Representation Learning for Click-Through Rate Prediction in E-Commerce Scenarios

Chao Yi, Feifan Yang, Jiawei Feng +4

Multimodal representations have been widely adopted in industrial e-commerce recommendation systems. Due to their strong semantic understanding and generalization capabilities, the…

cs.IR2026

DREAM Technical Report

Bin Zhang, Bowen Zheng, Chao Yi +74

Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…

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.IR2025

RecGPT-V2 Technical Report

Chao Yi, Dian Chen, Gaoyang Guo +32

Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. Whil…

cs.IR2025

MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling

Bin Wu, Feifan Yang, Zhangming Chan +8

Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization o…

cs.IR2025

RecGPT Technical Report

Chao Yi, Dian Chen, Gaoyang Guo +51

Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…