8 papers
Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models
Jia Deng, Junyi Li, Wayne Xin Zhao +3
Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training methods largely rely on random…
Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers
Bingqian Li, Bowen Zheng, Xiaolei Wang +5
Large language models (LLMs) have shown great promise in recommender systems, where supervised fine-tuning (SFT) is commonly used for adaptation. Subsequent studies further introdu…
Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation
Hao Guo, Erpeng Xue, Lei Huang +5
Deep Learning Recommendation Models (DLRMs) often rely on extensive manual feature engineering to improve accuracy and user experience, which increases system complexity and limits…
LARES: Latent Reasoning for Sequential Recommendation
Enze Liu, Bowen Zheng, Xiaolei Wang +4
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequ…
Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Xiaolei Wang, Chunxuan Xia, Junyi Li +5
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies i…
SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation
Lei Huang, Hao Guo, Linzhi Peng +7
We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for generative sequential recommendation, addressing the fundamental misalignment between conventional next…