5 papers
HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment
Guorui Li, Dugang Liu, Lei Li +2
Large language model (LLM)-enhanced sequential recommendation typically aims to improve two core components: user semantic embedding extraction and utilization. Despite promising r…
FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
Jun Zhang, Dugang Liu, Xing Tang +2
Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR). Most MMR methods ad…
Large Language Models for Multilingual Code Intelligence: A Survey
Chao Jiang, Dugang Liu, Cheng Wen +6
Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance…
Automated Information Flow Selection for Multi-scenario Multi-task Recommendation
Chaohua Yang, Dugang Liu, Shiwei Li +6
Multi-scenario multi-task recommendation (MSMTR) systems must address recommendation demands across diverse scenarios while simultaneously optimizing multiple objectives, such as c…
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Dugang Liu, Shenxian Xian, Xiaolin Lin +5
The training paradigm integrating large language models (LLM) is gradually reshaping sequential recommender systems (SRS) and has shown promising results. However, most existing LL…