110 citations · 264 across the 35 of their papers we have counts for
36 papers
Reward Guided Decoding for Generative Recommendation
Ruochen Yang, Yusheng Huang, Youfeng Zheng +11
Generative recommendation formulates recommendation task into an SID sequence autoregressive generation paradigm, but the decoding process is often dominated by generation likeliho…
Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence
Ruochen Yang, Shuang Wen, Pengbo Xu +6
Modern industrial recommendation systems typically separate recall and ranking into two independent stages. Although this cascade supports corpus-level retrieval and fine-grained m…
Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion
Jiawei Sheng, Taoyu Su, Xixun Lin +2
Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs. Existing methods typically…
Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
Xiaodong Li, Jiawei Sheng, Jiangxia Cao +6
Cross-domain recommendation (CDR) has demonstrated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a r…
Improving Reasoning Capabilities in Small Models through Mixture-of-Layers Distillation with Stepwise Attention on Key Information
Yao Chen, Jiawei Sheng, Wenyuan Zhang +1
The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation…
HyperMem: Hypergraph Memory for Long-Term Conversations
Juwei Yue, Chuanrui Hu, Jiawei Sheng +5
Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues. However, exi…
Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation
Yilong Chen, Naibin Gu, Junyuan Shang +8
Mixture-of-Experts (MoE) decouples model capacity from per-token computation, yet their scalability remains limited by the physical dimensions of depth and width. To overcome this,…
S2CDR: Smoothing-Sharpening Process Model for Cross-Domain Recommendation
Xiaodong Li, Juwei Yue, Xinghua Zhang +5
User cold-start problem is a long-standing challenge in recommendation systems. Fortunately, cross-domain recommendation (CDR) has emerged as a highly effective remedy for the user…
From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Ruochen Yang, Xiaodong Li, Jiawei Sheng +6
Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under…
COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence
Zefeng Zhang, Xiangzhao Hao, Hengzhu Tang +8
Visual Spatial Reasoning is crucial for enabling Multimodal Large Language Models (MLLMs) to understand object properties and spatial relationships, yet current models still strugg…
Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
Juwei Yue, Haikuo Li, Jiawei Sheng +4
Graph neural networks (GNNs) leverage message passing mechanisms to learn the topological features of graph data. Traditional GNNs learns node features in a spatial domain unrelate…
Graph Wave Networks
Juwei Yue, Haikuo Li, Jiawei Sheng +5
Dynamics modeling has been introduced as a novel paradigm in message passing (MP) of graph neural networks (GNNs). Existing methods consider MP between nodes as a heat diffusion pr…
Mitigating Modality Bias in Multi-modal Entity Alignment from a Causal Perspective
Taoyu Su, Jiawei Sheng, Duohe Ma +5
Multi-Modal Entity Alignment (MMEA) aims to retrieve equivalent entities from different Multi-Modal Knowledge Graphs (MMKGs), a critical information retrieval task. Existing studie…
Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization
Zefeng Zhang, Hengzhu Tang, Jiawei Sheng +6
Multimodal Large Language Models excel in various tasks, yet often struggle with modality bias, where the model tends to rely heavily on a single modality and overlook critical inf…
Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking
Yilong Chen, Junyuan Shang, Zhenyu Zhang +7
Large language models (LLMs) face inherent performance bottlenecks under parameter constraints, particularly in processing critical tokens that demand complex reasoning. Empirical…
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation
Xiaodong Li, Ruochen Yang, Shuang Wen +10
Live-streaming services have attracted widespread popularity due to their real-time interactivity and entertainment value. Users can engage with live-streaming authors by participa…
Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation
Xiaodong Li, Hengzhu Tang, Jiawei Sheng +5
Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user repres…
Mixture of Hidden-Dimensions Transformer
Yilong Chen, Junyuan Shang, Zhengyu Zhang +6
Transformer models encounter challenges in scaling hidden dimensions efficiently, as uniformly increasing them inflates computational and memory costs while failing to emphasize th…
Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing
Wenyuan Zhang, Shuaiyi Nie, Jiawei Sheng +4
Large language model (LLM) role-playing has gained widespread attention. Authentic character knowledge is crucial for constructing realistic LLM role-playing agents. However, exist…
LoginMEA: Local-to-Global Interaction Network for Multi-modal Entity Alignment
Taoyu Su, Xinghua Zhang, Jiawei Sheng +2
Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs (MMKGs), whose entities can be associated with relational triples…
IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
Taoyu Su, Jiawei Sheng, Shicheng Wang +3
Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs), where the entities can be associated with related images. Mos…
Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking
Zefeng Zhang, Jiawei Sheng, Chuang Zhang +4
Multimodal Entity Linking (MEL) aims to link ambiguous mentions in multimodal contexts to entities in a multimodal knowledge graph. A pivotal challenge is to fully leverage multi-e…
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion
Qian Li, Shu Guo, Yinjia Chen +3
Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty…
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
Xiaodong Li, Jiawei Sheng, Jiangxia Cao +3
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by tra…