Federated Learning for Cold Start Recommendations
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Author / Producer
Date
2024-01
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
With the increasing desire to protect the users' data and provide data privacy, few recent research works have been focusing on federated learning to avoid centralized training for various machine learning problems. Federated learning involving clients such as mobile devices has been widely explored and there is little exploration done toward training a federated model involving corporate companies or organizations. In this work, we want to understand the capability of a federated recommendation model using three news publishers' users' data to generate cold-start recommendations. We train a recommendation model using federated learning from three news publishers and evaluate the cold-start recommendation performance of our federated model against a model trained with each news publisher's data alone. Our results demonstrate that federated learning boosts the cold start recommendation for all news publishers and there is a higher ranking performance when federated with news publishers that have similar user behavior.
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Publication status
published
External links
Book title
CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)
Journal / series
Volume
Pages / Article No.
599 - 600
Publisher
Association for Computing Machinery
Event
7th Joint International Conference on Data Science & Management of Data (CODS-COMAD 2024)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Organisational unit
02154 - Media Technology Center (MTC) / Media Technology Center (MTC)