3 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
End-to-end training of Multimodal Model and ranking Model
Xiuqi Deng, Lu Xu, Xiyao Li +10
Traditional recommender systems heavily rely on ID features, which often encounter challenges related to cold-start and generalization. Modeling pre-extracted content features can…