26 citations · 26 across the 12 of their papers we have counts for
14 papers
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
Chengqi Yang, Yiran Qiao, Feng Liu +7
User representation pre-training has become a fundamental paradigm for alleviating data sparsity in downstream personalization tasks. However, existing studies predominantly focus…
Discrete Preference Learning for Personalized Multimodal Generation
Yuting Zhang, Ying Sun, Dazhong Shen +6
The emergence of generative models enables the creation of texts and images tailored to users' preferences. Existing personalized generative models have two critical limitations: l…
User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
Jing Du, Zesheng Ye, Congbo Ma +2
Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. Th…
SpecTran: Spectral-Aware Transformer-based Adapter for LLM-Enhanced Sequential Recommendation
Yu Cui, Feng Liu, Zhaoxiang Wang +4
Traditional sequential recommendation (SR) models learn low-dimensional item ID embeddings from user-item interactions, often overlooking textual information such as item titles or…
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
Feng Liu, Hao Cang, Huanhuan Yuan +5
Spectral graph neural networks (GNNs) are highly effective in modeling graph signals, with their success in recommendation often attributed to low-pass filtering. However, recent s…
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…
Does LLM Focus on the Right Words? Mitigating Context Bias in LLM-based Recommenders
Bohao Wang, Jiawei Chen, Feng Liu +5
Large language models (LLMs), owing to their extensive open-domain knowledge and semantic reasoning capabilities, have been increasingly integrated into recommender systems (RS). H…
Flow Matching based Sequential Recommender Model
Feng Liu, Lixin Zou, Xiangyu Zhao +5
Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due…
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…
OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning
Zhiyuan Liu, Yuting Zhang, Feng Liu +3
Multimodal Large Language Models (MLLMs) have gained significant traction for their ability to process diverse input data types and generate coherent, contextually relevant outputs…
Adaptive Conditional Expert Selection Network for Multi-domain Recommendation
Kuiyao Dong, Xingyu Lou, Feng Liu +4
Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typic…
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…
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model
Yu Cui, Feng Liu, Pengbo Wang +5
Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the…