5 papers
Multi Interests for Joint Search-Recommendation Modeling
Xiangchen Pan, Wei Wei, Huakang Niu +1
Search and recommendation are crucial for understanding user preferences. More and more studies are attempting to jointly model search behavior and recommendation behavior, by inte…
Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation
Xiangchen Pan, Wei Wei
Explainable recommendation systems (RSs) are designed to explicitly uncover the rationale of each recommendation, thereby enhancing the transparency and credibility of RSs. Previou…
User Simulator-Guided Multi-Turn Preference Optimization for Reasoning LLM-based Conversational Recommendation
Xingyuan Xiang, Xiangchen Pan, Wei Wei
Conversational Recommender Systems (CRSs) leverage natural language interactions for personalized recommendation, yet information-scarce dialogue histories and single-turn recommen…
MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation
Xiangchen Pan, Wei Wei
Explainable recommendations help improve the transparency and credibility of recommendation systems, and play an important role in personalized recommendation scenarios. At present…
Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation
Xiangchen Pan, Wei Wei
In recent years, multimodal recommendation has received significant attention and achieved remarkable success in GCN-based recommendation methods. However, there are two key challe…