Distributed Recommendation Systems: Survey and Research Directions

With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Information Systems
Published
2024-09-06
DOI
https://doi.org/10.1145/3694783
Citations
3
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
2.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Distributed Recommendation Systems: Survey and Research Directions

Nengjun Zhu, Guandong Xu, Jian Cao, Qiqi Cai
3 citations
ACM Transactions on Information Systems
Recommender Systems and Techniques
2.34
article

Distributed Recommendation Systems: Survey and Research Directions

Nengjun Zhu, Guandong Xu, Jian Cao, Qiqi Cai
article en
3 citations

Abstract

With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.

ACM Transactions on Information SystemsVol. 43(1)
University of Technology Sydney (AU), Shanghai University of Engineering Science (CN), Shanghai Jiao Tong University (CN), Education University of Hong Kong (HK)
Openalex Percentile: Top 9%
Recommender Systems and Techniques
2.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.