Name
Abstract | ||
|---|---|---|
Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while preserving user privacy. In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, instead of model or data adopted in previous approaches. In this framework, user-specific recommendation models are locally trained by a shared parameterized algorithm, which preserves user privacy and at the same time utilizes information from other users to help model training. Interestingly, the model thus trained exhibits a high capacity at a small scale, which is energy- and communication-efficient. Experimental results show that recommendation models trained by meta-learning algorithms in the proposed framework outperform the state-of-the-art in accuracy and scale. For example, on a production dataset, a shared model under Google Federated Learning (McMahan et al., 2017) with 900,000 parameters has prediction accuracy 76.72%, while a shared algorithm under federated meta-learning with less than 30,000 parameters achieves accuracy of 86.23%. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Recommender system,User information,Artificial intelligence,User privacy,Machine learning,Mathematics,Parameterized algorithms |
DocType | Volume | Citations |
Journal | abs/1802.07876 | 6 |
PageRank | References | Authors |
0.50 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Chen | 1 | 57 | 4.57 |
| Zhenhua Dong | 2 | 91 | 9.03 |
| Zhenguo Li | 3 | 581 | 41.17 |
| Xiuqiang He | 4 | 6 | 0.84 |