6 citations · 10 across the 24 of their papers we have counts for
7 papers · 1 filter
HatLLM: Hierarchical Attention Masking for Enhanced Collaborative Modeling in LLM-based Recommendation
Yu Cui, Feng Liu, Jiawei Chen +6
Recent years have witnessed a surge of research on leveraging large language models (LLMs) for sequential recommendation. LLMs have demonstrated remarkable potential in inferring u…
Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction
Yu Cui, Feng Liu, Jiawei Chen +6
Click-through rate (CTR) prediction is a fundamental task in modern recommender systems. In recent years, the integration of large language models (LLMs) has been shown to effectiv…
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Bohao Wang, Feng Liu, Jiawei Chen +7
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundament…
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
Jiaqing Zhang, Mingjia Yin, Hao Wang +5
In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on l…
DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
Heyuan Huang, Xingyu Lou, Chaochao Chen +5
Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an i…
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Changwang Zhang +8
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy i…
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Taotian Pang, Xingyu Lou, Fei Zhao +5
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. How…