3 citations · 7 across the 28 of their papers we have counts for
16 papers · 1 filter
Closing the Long-Short View Gap in Sequential Recommendation without Cached History
Lingfeng Shi, Chengkai Huang, Lina Yao +1
Sequential recommenders are typically trained on long user histories to capture rich behavioral signals, yet serving with training-length sequences is often impractical due to real…
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…
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…
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…
Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
Hongtao Huang, Chengkai Huang, Junda Wu +3
Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…
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…
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…
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…
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
Chengkai Huang, Hongtao Huang, Tong Yu +6
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…
Dual Conditional Diffusion Models for Sequential Recommendation
Hongtao Huang, Chengkai Huang, Tong Yu +4
Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). Existing approaches predominantly rely on implicit conditional diffusion mod…
Foundation Models for Recommender Systems: A Survey and New Perspectives
Chengkai Huang, Tong Yu, Kaige Xie +3
Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).…