Search Results for author:
Found 9 papers, 7 papers with code
Federated Adaptation for Foundation Model-based Recommendations
It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy.
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives.
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings.
GPFedRec: Graph-guided Personalization for Federated Recommendation
The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner.
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.
Dual Personalization on Federated Recommendation
Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings.
Curvature Regularization to Prevent Distortion in Graph Embedding
Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space.
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks
Although we can benefit a lot from DA, designing appropriate DA policies requires a lot of expert experience and time consumption, and the evaluation of searching the optimal policies is costly.
