1 citations · 3 across the 15 of their papers we have counts for
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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…
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…
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…