期刊
INFORMATION SCIENCES
卷 601, 期 -, 页码 189-206出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.04.027
关键词
Federated learning; Client selection; Recommendation system; Cold-start problem; Deep reinforcement learning; Trust establishment
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
This paper proposes a federated learning-based approach to address the problem of cold-start items in recommendation systems. The uniqueness of this approach lies in its specific application to the cold-start problem and the introduction of a trust mechanism and a double deep Q learning scheduling method based on trust and energy levels. Simulation experiments show that this method improves the accuracy of recommending cold-start items.
Recommendation systems are often challenged by the existence of cold-start items for which no previous rating is available. The standard content-based or collaborative-filtering recommendation approaches may address this problem by asking users to share their data with a central (cloud-based) server, which uses machine learning to predict appropriate ratings on such items. But users may be reluctant to have their (confidential) data shared. Federated learning has been lately capitalized on to address the privacy concerns by enabling an on-device distributed training of a single machine learning model. In this work, we propose a federated learning-based approach to address the item cold-start problem in recommendation systems. The originality of our solution compared to existing federated learning-based solutions comes from (1) applying federated learning specifically to the cold-start problem; (2) proposing a trust mechanism to derive trust scores for the potential recommenders, followed by a double deep Q learning scheduling approach that relies on the trust and energy levels of the recommenders to select the best candidates. Simulations on the MovieLens 1M and Epinions datasets suggest that our solution improves the accuracy of recommending cold-start items and reduces the RMSE, MAE and running time compared to five benchmark approaches. (C) 2022 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据