17 citations · 35 across the 9 of their papers we have counts for
9 papers
MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan
Xin Song, Zhilin Guan, Ruidong Han +12
Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and str…
A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation
Xiaoyu Liu, Yiqing Wu, Ruidong Han +3
In industrial practice, Multi-domain Recommendation (MDR) plays a crucial role. Shared-specific architectures are widely used in industrial solutions to capture shared and unique a…
MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
Ruidong Han, Bin Yin, Shangyu Chen +12
Scaling law has been extensively validated in many domains such as natural language processing and computer vision. In the recommendation system, recent work has adopted generative…
MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan
Yuxiang Wang, Chi Ma, Xiao Yan +15
Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce q…
Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
Yurou Zhao, Yiding Sun, Ruidong Han +6
Providing natural language-based explanations to justify recommendations helps to improve users' satisfaction and gain users' trust. However, as current explanation generation meth…
Adaptive Utilization of Cross-scenario Information for Multi-scenario Recommendation
Xiufeng Shu, Ruidong Han, Xiang Li +1
Recommender system of the e-commerce platform usually serves multiple business scenarios. Multi-scenario Recommendation (MSR) is an important topic that improves ranking performanc…
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework
Ruidong Han, Qianzhong Li, He Jiang +4
Understanding user interests is crucial for Click-Through Rate (CTR) prediction tasks. In sequential recommendation, pre-training from user historical behaviors through self-superv…
Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation
Yuting Zhang, Yiqing Wu, Ruidong Han +7
Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively,…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…