6 papers · 1 filter
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation
Jiaju Chen, Chongming Gao, Chenxiao Fan +4
Large language model (LLM)-based generative list-wise recommendation has advanced rapidly, but decoding remains sequential and thus latency-prone. To accelerate inference without c…
Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation
Yaxin Gong, Chongming Gao, Chenxiao Fan +6
Large language models (LLMs) have spurred interest in agent-based recommender systems, yet most agentic approaches remain user-centric: items stay passive entities whose exposure i…
Uncertainty-aware Generative Recommendation
Chenxiao Fan, Chongming Gao, Yaxin Gong +3
Generative Recommendation has emerged as a transformative paradigm, reformulating recommendation as an end-to-end autoregressive sequence generation task. Despite its promise, exis…
Process-Supervised LLM Recommenders via Flow-guided Tuning
Chongming Gao, Mengyao Gao, Chenxiao Fan +3
While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likeliho…
Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription
Zihao Zhao, Chenxiao Fan, Junlong Liu +5
Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flex…