17 citations · 23 across the 6 of their papers we have counts for
6 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…
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…
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
Zhizhong Wan, Bin Yin, Junjie Xie +3
Click-Through Rate (CTR) prediction is crucial for Recommendation System(RS), aiming to provide personalized recommendation services for users in many aspects such as food delivery…
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…
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Di Jin, Luzhi Wang, Yizhen Zheng +5
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations n…
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Di Jin, Luzhi Wang, Yizhen Zheng +4
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classifica…