6 citations · 9 across the 17 of their papers we have counts for
7 papers · 1 filter
Intuition-Guided Latent Reasoning for LLM-Based Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender syst…
Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders
Wentao Shi, Qifan Wang, Chen Chen +7
Reinforcement learning (RL) effectively optimizes Large Language Model (LLM)-based recommenders by contrasting positive and negative items. Empirically, training with beam-search n…
Bringing Reasoning to Generative Recommendation Through the Lens of Cascaded Ranking
Xinyu Lin, Pengyuan Liu, Wenjie Wang +5
Generative Recommendation (GR) has become a promising end-to-end approach with high FLOPS utilization for resource-efficient recommendation. Despite the effectiveness, we show that…
K-order Ranking Preference Optimization for Large Language Models
Shihao Cai, Chongming Gao, Yang Zhang +5
To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order…
Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation
Yi Fang, Wenjie Wang, Yang Zhang +4
Aligning Large Language Models (LLMs) with recommendation tasks represents an emerging paradigm in recommendation domain, exhibiting promising performance overall. However, these a…
Agentic Feedback Loop Modeling Improves Recommendation and User Simulation
Shihao Cai, Jizhi Zhang, Keqin Bao +4
Large language model-based agents are increasingly applied in the recommendation field due to their extensive knowledge and strong planning capabilities. While prior research has p…