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Federated Low-rank Tensor Projections for Sequential Recommendation by Li Li; Fan Lin; Jianbing Xiahou; Yuanguo Lin; Pengcheng Wu; Yong Liu is a Computer Science article available to read on EtoBox.

What is Federated Low-rank Tensor Projections for Sequential Recommendation about?

Recommender systems have achieved great success in many fields, benefiting from the accessibility of massive amounts of user behavior data. However, the increasing concern about privacy issues brings new research challenges to traditional data-centralized recommender systems. As a privacy-preserving machine learning technique, federated learning provides potential solutions to these research challenges, by jointly training perceptive global recommendation models based on the decentralized user behavior data. Although existing federated recommendation methods have achieved promising results, they still suffer the following limitations. Firstly, they mainly focus on the horizontal federated recommendation scenarios where the participants are individual users, without considering the scenarios where participants are organizations. Secondly, they tend to model static user preferences and cannot learn the preference evolution in users’ interaction sequences. To address these limitations, we propose a horizontal Federated recommendation framework for Sequential Recommendation (FedSeqRec), where the participants are organizations. As the data in different organizations are usually not ind

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Author

Li Li; Fan Lin; Jianbing Xiahou; Yuanguo Lin; Pengcheng Wu; Yong Liu

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)