3 citations · 6 across the 15 of their papers we have counts for
8 papers · 1 filter
Recommender System as Slow and Fast Thinkers
Zichen Yuan, Xiaoxuan Dong, Linkun Dai +8
Sequential recommendation models are foundational to modern personalized services, yet their effectiveness varies substantially across heterogeneous user environments. In particula…
Risk-Aware Reranking for Agentic Tool Retrieval
Qinfei Li, Xiaoxuan Dong, Jin Zhang +6
Tool retrieval determines which external tools are exposed to an LLM agent for a user query or task, making retrieval a critical pre-execution safety boundary. Unlike document retr…
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration
Hongji Li, Hanwen Du, Youhua Li +5
The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities such as text, images, videos, and audio for mor…
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
Junchen Fu, Yongxin Ni, Joemon M. Jose +4
In this paper, we explore a less-studied yet practically important problem: how to efficiently and effectively adapt multiple (2) multimodal foundation models (MFMs) for the seq…
An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders
Youhua Li, Hanwen Du, Yongxin Ni +4
Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the hu…
Multi-Modality is All You Need for Transferable Recommender Systems
Youhua Li, Hanwen Du, Yongxin Ni +4
ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys.…
A Content-Driven Micro-Video Recommendation Dataset at Scale
Yongxin Ni, Yu Cheng, Xiangyan Liu +5
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and…
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Jiaqi Zhang, Yu Cheng, Yongxin Ni +6
Large foundational models, through upstream pre-training and downstream fine-tuning, have achieved immense success in the broad AI community due to improved model performance and s…