7 citations · 15 across the 13 of their papers we have counts for
13 papers
FedUTR: Federated Recommendation with Augmented Universal Textual Representation for Sparse Interaction Scenarios
Kang Fu, Honglei Zhang, Zikai Zhang +5
Federated recommendations (FRs) have emerged as an on-device privacy-preserving paradigm, attracting considerable attention driven by rising demands for data security. Existing FRs…
Semantic Item Graph Enhancement for Multimodal Recommendation
Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2
Multimodal recommendation systems have attracted increasing attention for their improved performance by leveraging items' multimodal information. Prior methods often build modality…
CM: Calibrating Multimodal Recommendation
Xin Zhou, Yongjie Wang, Zhiqi Shen
Alignment and uniformity are fundamental principles within the domain of contrastive learning. In recommender systems, prior work has established that optimizing the Bayesian Perso…
EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation
Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2
MultiModal Recommendation (MMR) systems have emerged as a promising solution for improving recommendation quality by leveraging rich item-side modality information, prompting a sur…
Response Uncertainty and Probe Modeling: Two Sides of the Same Coin in LLM Interpretability?
Yongjie Wang, Yibo Wang, Xin Zhou +1
Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. However, the factors governing a dat…
Learning Item Representations Directly from Multimodal Features for Effective Recommendation
Xin Zhou, Xiaoxiong Zhang, Dusit Niyato +1
Conventional multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations by amalgamating item identity (ID)…
GiFT: Gibbs Fine-Tuning for Code Generation
Haochen Li, Wanjin Feng, Xin Zhou +1
Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. A key approach is self-training, where LLMs are iteratively trained on self-ge…
Advancing Sustainability via Recommender Systems: A Survey
Xin Zhou, Lei Zhang, Honglei Zhang +4
Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to tra…
Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou +1
Federated Knowledge Graph Embedding (FKGE) aims to facilitate collaborative learning of entity and relation embeddings from distributed Knowledge Graphs (KGs) across multiple clien…
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification
Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou +2
Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication…
Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph
Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou +2
Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable interest due to its capacity to extract expressive representations from distributed knowledge graphs,…
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Honglei Zhang, Haoxuan Li, Jundong Chen +6
Federated recommendation aims to collect global knowledge by aggregating local models from massive devices, to provide recommendations while ensuring privacy. Current methods mainl…
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning
Lingzi Zhang, Xin Zhou, Zhiwei Zeng +1
Current multimodal sequential recommendation models are often unable to effectively explore and capture correlations among behavior sequences of users and items across different mo…