1 citations · 3 across the 21 of their papers we have counts for
23 papers
Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation
Fei Li, Qingyun Gao, Jianzhe Zhao +5
Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation. Existing methods generally fall int…
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
Yuting Liu, Wei Wu, Jianzhe Zhao +1
Natural language user preferences provide an interpretable interface for LLM personalization. However, universal preference summaries often contain information irrelevant to a part…
PCTD: Preference-Guided Counterfactual Task Decomposition for Agent Tool Retrieval
Chu Zhao, Lei Tang, Minghang Li +5
Task decomposition aims to transform ambiguous instructions into executable atomic subtasks, thereby guiding high-precision tool retrieval. However, our analysis reveals that direc…
Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
Chu Zhao, Enneng Yang, Jianzhe Zhao +1
Direct Preference Optimization (DPO) guides large language models (LLMs) to generate recommendations aligned with user historical behavior distributions by minimizing preference al…
ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
Chu Zhao, Enneng Yang, Yuting Liu +2
Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels constructed by majority voting. To reduc…
Tail-Aware Data Augmentation for Long-Tail Sequential Recommendation
Yizhou Dang, Zhifu Wei, Minhan Huang +4
Sequential recommendation (SR) learns user preferences based on their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most users ca…
Text as a Universal Interface for Transferable Personalization
Yuting Liu, Jian Guan, Jia-Nan Li +4
We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yie…
FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarios
Jianzhe Zhao, Hailin Zhu, Yu Zhang +2
Federated efficient fine-tuning has emerged as an approach that leverages distributed data and computational resources across nodes to address the challenges of large-scale fine-tu…
Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang +3
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward l…
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Data augmentation has become a promising method of mitigating data sparsity in sequential recommendation. Existing methods generate new yet effective data during model training to…
Hard Negative Sampling via Large Language Models for Recommendation
Chu Zhao, Enneng Yang, Yuting Liu +2
Hard negative sampling improves recommendation performance by accelerating convergence and sharpening the decision boundary. However, most existing methods rely on heuristic strate…
Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product
Pengxiang Lan, Haoyu Xu, Enneng Yang +4
Prompt tuning (PT) offers a cost-effective alternative to fine-tuning large-scale pre-trained language models (PLMs), requiring only a few parameters in soft prompt tokens added be…
Data Assetization via Resources-decoupled Federated Learning
Jianzhe Zhao, Feida Zhu, Lingyan He +4
With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to…
Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
Chu Zhao, Enneng Yang, Yuliang Liang +3
The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's…
Augmenting Sequential Recommendation with Balanced Relevance and Diversity
Yizhou Dang, Jiahui Zhang, Yuting Liu +5
By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on au…
Self-supervised Hierarchical Representation for Medication Recommendation
Yuliang Liang, Yuting Liu, Yizhou Dang +5
Medication recommender is to suggest appropriate medication combinations based on a patient's health history, e.g., diagnoses and procedures. Existing works represent different dia…
Data Augmentation for Sequential Recommendation: A Survey
Yizhou Dang, Enneng Yang, Yuting Liu +4
As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the wides…
Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +3
Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consiste…
CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation
Yuting Liu, Jinghao Zhang, Yizhou Dang +5
Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating co…
Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +5
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. Ho…
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
Pengxiang Lan, Enneng Yang, Yuting Liu +3
Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, w…
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
Yuting Liu, Yizhou Dang, Yuliang Liang +4
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (…
Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted t…