Dual Personalization on Federated Recommendation
- URL: http://arxiv.org/abs/2301.08143v2
- Date: Sat, 13 May 2023 08:23:17 GMT
- Title: Dual Personalization on Federated Recommendation
- Authors: Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang,
Chengqi Zhang, Bo Yang
- Abstract summary: Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings.
This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models.
We also propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items.
- Score: 50.4115315992418
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated recommendation is a new Internet service architecture that aims to
provide privacy-preserving recommendation services in federated settings.
Existing solutions are used to combine distributed recommendation algorithms
and privacy-preserving mechanisms. Thus it inherently takes the form of
heavyweight models at the server and hinders the deployment of on-device
intelligent models to end-users. This paper proposes a novel Personalized
Federated Recommendation (PFedRec) framework to learn many user-specific
lightweight models to be deployed on smart devices rather than a heavyweight
model on a server. Moreover, we propose a new dual personalization mechanism to
effectively learn fine-grained personalization on both users and items. The
overall learning process is formulated into a unified federated optimization
framework. Specifically, unlike previous methods that share exactly the same
item embeddings across users in a federated system, dual personalization allows
mild finetuning of item embeddings for each user to generate user-specific
views for item representations which can be integrated into existing federated
recommendation methods to gain improvements immediately. Experiments on
multiple benchmark datasets have demonstrated the effectiveness of PFedRec and
the dual personalization mechanism. 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.
The code is available.
Related papers
- Personalized Item Representations in Federated Multimodal Recommendation [37.52127488593226]
Federated Multimodal Recommendation System, called FedMR, encodes multimodal item data.
FedMR is compatible with existing ID-based federated recommendation systems.
Experiments on four real-world multimodal datasets demonstrate FedMR's effectiveness.
arXiv Detail & Related papers (2024-10-11T03:10:09Z) - Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy.
This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously.
To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
arXiv Detail & Related papers (2024-08-16T05:49:14Z) - Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation [22.359428566363945]
Federated recommendation aims to collect global knowledge by aggregating local models from massive devices.
Current methods mainly leverage aggregation functions invented by federated vision community to aggregate parameters from similar clients.
We propose a personalized Federated recommendation model with Composite Aggregation (FedCA)
arXiv Detail & Related papers (2024-06-06T10:17:52Z) - A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation [77.42486522565295]
We propose a novel recommendation approach called LSVCR to jointly conduct personalized video and comment recommendation.
Our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender.
In particular, we achieve a significant overall gain of 4.13% in comment watch time.
arXiv Detail & Related papers (2024-03-20T13:14:29Z) - User Consented Federated Recommender System Against Personalized
Attribute Inference Attack [55.24441467292359]
We propose a user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users.
UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent.
arXiv Detail & Related papers (2023-12-23T09:44:57Z) - MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation [46.0605442943949]
A common challenge for most recommender systems is the cold-start problem.
In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories.
We adopt a meta-optimization approach for optimizing the proposed method.
arXiv Detail & Related papers (2020-07-07T03:25:15Z) - Federated Multi-view Matrix Factorization for Personalized
Recommendations [53.74747022749739]
We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources.
Our method is able to learn the multi-view model without transferring the user's personal data to a central server.
arXiv Detail & Related papers (2020-04-08T21:07:50Z) - MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive
Model Selection [110.87712780017819]
We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems.
We conduct experiments on two public datasets and a real-world production dataset.
arXiv Detail & Related papers (2020-01-22T16:05:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.