4 citations · 9 across the 6 of their papers we have counts for
8 papers · 1 filter
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…
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…