2 citations · 5 across the 13 of their papers we have counts for
15 papers
Federated Semantic Residuals for Multimodal Recommendation
Zhiwei Li, Guodong Long, Chengqi Zhang +1
Federated Recommendation Systems (FedRSs) learn collaborative scores from interaction histories on device and rank items by them, so a higher score means a higher rank. Item text a…
Symmetric Bases and Embeddings in Musielak--Orlicz Sequence Spaces under
Valentin Georgiev, Boyan Zlatanov
We study isomorphic embeddings into Musielak-Orlicz sequence spaces \(\ell_Φ\), where the normalized generating sequence \(Φ=\{ϕ_n\}\) satisfies the \(δ_2\) condition. Using admiss…
Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy
Liwei Deng, Jing Jiang, Zhiwei Li +2
Driven by the attention economy, short-video Recommender Systems (RSs) are primarily optimized to maximize user engagement by promoting videos that capture attention within seconds…
Federated Weather Modeling on Sensor Data
Shengchao Chen, Guodong Long
Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including ground weather stations, satel…
Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation
Chunxu Zhang, Zhiheng Xue, Guodong Long +2
User-centric recommendation has become essential for delivering personalized services, as it enables systems to adapt to users' evolving behaviors while respecting their long-term…
Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
Chunxu Zhang, Weipeng Zhang, Guodong Long +3
Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner. How to integrate multi-modality features into federat…
Distilling A Universal Expert from Clustered Federated Learning
Zeqi Leng, Chunxu Zhang, Guodong Long +2
Clustered Federated Learning (CFL) addresses the challenges posed by non-IID data by training multiple group- or cluster-specific expert models. However, existing methods often ove…
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
Zhiwei Li, Guodong Long, Chunxu Zhang +3
Integrating Foundation Models (FMs) into recommendation systems is an emerging and promising research direction. However, centralized paradigms face growing pressure from privacy c…
Personalized Recommendation Models in Federated Settings: A Survey
Chunxu Zhang, Guodong Long, Zijian Zhang +4
Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experie…
Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
Chunxu Zhang, Guodong Long, Hongkuan Guo +5
Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, p…
Federated Vision-Language-Recommendation with Personalized Fusion
Zhiwei Li, Guodong Long, Jing Jiang +2
Applying large pre-trained Vision-Language Models to recommendation is a burgeoning field, a direction we term Vision-Language-Recommendation (VLR). Bringing VLR to user-oriented o…
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Zhiwei Li, Guodong Long, Tianyi Zhou +2
Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy in a federated setting. Existing FedCF m…
Navigating the Future of Federated Recommendation Systems with Foundation Models
Zhiwei Li, Guodong Long, Chunxu Zhang +3
Federated Recommendation Systems (FRSs) offer a privacy-preserving alternative to traditional centralized approaches by decentralizing data storage. However, they face persistent c…
Federated Adaptation for Foundation Model-based Recommendations
Chunxu Zhang, Guodong Long, Hongkuan Guo +7
With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradig…
GPFedRec: Graph-guided Personalization for Federated Recommendation
Chunxu Zhang, Guodong Long, Tianyi Zhou +3
The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhan…