2 citations · 2 across the 5 of their papers we have counts for
5 papers
LARES: Latent Reasoning for Sequential Recommendation
Enze Liu, Bowen Zheng, Xiaolei Wang +4
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequ…
Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation
Hao Guo, Erpeng Xue, Lei Huang +5
Deep Learning Recommendation Models (DLRMs) often rely on extensive manual feature engineering to improve accuracy and user experience, which increases system complexity and limits…
SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation
Lei Huang, Hao Guo, Linzhi Peng +7
We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for generative sequential recommendation, addressing the fundamental misalignment between conventional next…
EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation
Lei Huang, Weitao Li, Chenrui Zhang +3
Cross-domain recommendation has attracted substantial interest in industrial apps such as Meituan, which serves multiple business domains via knowledge transfer and meets the diver…
Knowledge Graph Driven Recommendation System Algorithm
Chaoyang Zhang, Yanan Li, Shen Chen +2
In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiv…