8 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…
SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests
Wei Zhou, Yue Shen, Junkai Ji +5
User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven tem…
PreferRec: Learning and Transferring Pareto Preferences for Multi-objective Re-ranking
Wei Zhou, Wuyang Li, Junkai Ji +5
Multi-objective re-ranking has become a critical component of modern multi-stage recommender systems, as it tasked to balance multiple conflicting objectives such as accuracy, dive…
Give Users the Wheel: Towards Promptable Recommendation Paradigm
Fuyuan Lyu, Chenglin Luo, Qiyuan Zhang +6
Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these architectures remain structurally blind to exp…
Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting
Fuyuan Lyu, Linfeng Du, Yunpeng Weng +6
Fund allocation has been an increasingly important problem in the financial domain. In reality, we aim to allocate the funds to buy certain assets within a certain future period. N…