5 citations · 15 across the 6 of their papers we have counts for
6 papers
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…
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,…
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
Zhichao Feng, Junjiie Xie, Kaiyuan Li +7
In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectivel…
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…