11 papers
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…