9 citations · 38 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
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,…
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…
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
Bin Yin, Junjie Xie, Yu Qin +4
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of h…
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…
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…