3 papers
cs.IR2025
MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation
Xianyang Qi, Yuan Tian, Zhaoyu Hu +4
Industrial recommender systems critically depend on high-quality ranking models. However, traditional pipelines still rely on manual feature engineering and scenario-specific archi…
cs.IR2025
Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation
Zhaoyu Hu, Jianyang Wang, Hao Guo +6
In the context of the booming digital economy, recommendation systems, as a key link connecting users and numerous services, face challenges in modeling user behavior sequences on…
cs.IR2025
When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation
Hongxiang Lin, Hao Guo, Zeshun Li +6
Traditional recommendation systems tend to trap users in strong feedback loops by excessively pushing content aligned with their historical preferences, thereby limiting exploratio…