8 citations · 10 across the 10 of their papers we have counts for
10 papers
TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation
Pengyu Zhang, Yangqin Jiang, Klim Zaporojets +2
Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates…
AgentBrew: Lifelong Knowledge Brewing from Strong Teachers to Weak LLM Agents
Yangqin Jiang, Chao Huang
Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is available during training. We study knowledge brewing: distilling a teacher's int…
AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets
Tianyu Fan, Yuhao Yang, Yangqin Jiang +3
Large Language Models (LLMs) have demonstrated remarkable potential as autonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. Ho…
OpenPhone: Mobile Agentic Foundation Models
Yangqin Jiang, Chao Huang
With the advancement of multimodal large language models (MLLMs), building GUI agent systems has become an increasingly promising direction--especially for mobile platforms, given…
RecGPT: A Foundation Model for Sequential Recommendation
Yangqin Jiang, Xubin Ren, Lianghao Xia +3
This work addresses a fundamental barrier in recommender systems: the inability to generalize across domains without extensive retraining. Traditional ID-based approaches fail enti…
RecLM: Recommendation Instruction Tuning
Yangqin Jiang, Yuhao Yang, Lianghao Xia +3
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Net…
DiffMM: Multi-Modal Diffusion Model for Recommendation
Yangqin Jiang, Lianghao Xia, Wei Wei +3
The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, a…
PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning
Wei Wei, Jiabin Tang, Yangqin Jiang +2
Multimedia online platforms (e.g., Amazon, TikTok) have greatly benefited from the incorporation of multimedia (e.g., visual, textual, and acoustic) content into their personal rec…
DiffKG: Knowledge Graph Diffusion Model for Recommendation
Yangqin Jiang, Yuhao Yang, Lianghao Xia +1
Knowledge Graphs (KGs) have emerged as invaluable resources for enriching recommendation systems by providing a wealth of factual information and capturing semantic relationships a…
Adaptive Graph Contrastive Learning for Recommendation
Yangqin Jiang, Chao Huang, Lianghao Xia
Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is…