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cs.IR2026
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…
cs.IR2026
Verifiable Reasoning for LLM-based Generative Recommendation
Xinyu Lin, Hanqing Zeng, Hanchao Yu +8
Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing…
cs.IR2024★ 5 cited
Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models
Fei Liu, Zejun Kang, Xing Han
With the growing demand for offline PDF chatbots in automotive industrial production environments, optimizing the deployment of large language models (LLMs) in local, low-performan…