2 citations · 5 across the 13 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…