1 citations · 1 across the 15 of their papers we have counts for
16 papers
Multimodal Graph Retrieval-Augmented Sequential Recommendation via Collaborative Filtering Paths
Jason Marcell Setiadi, Xin Cao, Lina Yao
Multimodal Large Language Models (MLLMs) have demonstrated strong potential for sequential recommendation through their ability to reason over complex multimodal data. However, exi…
From Classification to Recommendation: Empirical Analysis of Audio Embedding Models Application for Content-Based Music Recommendation
Qingrui Li, Haowei Lou, Chengkai Huang +2
Pretrained audio representation models learned from large-scale corpora have achieved strong performance in audio classification and understanding. However, most existing models ar…
Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation
Hao Cong, Huizu Lin, Zihan Wang +3
Large language model (LLM)-based agentic recommender systems show promise in modeling user preferences through natural-language reasoning, yet they remain limited by text-centric i…
Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs
Chengkai Huang, Tianqi Gao, Hongtao Huang +2
Semantic-ID-based generative recommendation has recently emerged as a scalable paradigm for sequential recommendation, where each item is represented by a compact sequence of discr…
Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
Xihang Wang, Zihan Wang, Chengkai Huang +4
Multimodal Retrieval-Augmented Generation (MRAG) is widely adopted for Multimodal Large Language Models (MLLMs) with external evidence to reduce hallucinations. Despite its success…
MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG
Xihang Wang, Zihan Wang, Chengkai Huang +2
Multimodal Retrieval-Augmented Generation (MRAG) addresses key limitations of Multimodal Large Language Models (MLLMs), such as hallucination and outdated knowledge. However, curre…
Factorized Latent Reasoning for LLM-based Recommendation
Tianqi Gao, Chengkai Huang, Zihan Wang +3
Large language models (LLMs) have recently been adopted for recommendation by framing user preference modeling as a language generation problem. However, existing latent reasoning…
Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generation
Shuguang Jiao, Chengkai Huang, Shuhan Qi +6
Agentic Retrieval-Augmented Generation interleaves retrieval and reasoning for multi-hop QA and complex knowledge tasks. As reasoning trajectories lengthen, failures become more fr…
Generative Chain of Behavior for User Trajectory Prediction
Chengkai Huang, Xiaodi Chen, Hongtao Huang +2
Modeling long-term user behavior trajectories is essential for understanding evolving preferences and enabling proactive recommendations. However, most sequential recommenders focu…
MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning
Juexiang Ye, Xue Li, Xinyu Yang +4
Large language model-based agents operating in long-horizon interactions require memory systems that support temporal consistency, multi-hop reasoning, and evidence-grounded reuse…
PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation
Shuguang Jiao, Xinyu Xiao, Yunfan Wei +4
Retrieval-augmented generation (RAG) has become a powerful framework for enhancing large language models in knowledge-intensive and reasoning tasks. However, as reasoning chains de…
Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation
Xiaoxin Ye, Chengkai Huang, Hongtao Huang +1
Users increasingly interact with content across multiple domains, resulting in sequential behaviors marked by frequent and complex transitions. While Cross-Domain Sequential Recomm…
Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registration
Xingmei Wang, Xiaoyu Hu, Chengkai Huang +4
Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semant…
Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation
Xiaoxin Ye, Chengkai Huang, Hongtao Huang +1
Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across domains to enhance recommendation quality. However, naive aggregation of sequential signals can introd…
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang +5
For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the…
Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systems
Le Pan, Yuanjiang Cao, Chengkai Huang +2
Recommender Systems (RSs) aim to provide personalized recommendations for users. A newly discovered bias, known as sentiment bias, uncovers a common phenomenon within Review-based…