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Distributed Recommendation Systems: Survey and Research Directions
Authors: 
Qiqi Cai, Jian Cao, Guandong Xu, Nengjun ZhuAuthors Info & Claims
Qiqi Cai, Jian Cao, Guandong Xu, Nengjun ZhuAuthors Info & ClaimsAbstract
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.
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Index Terms
- Distributed Recommendation Systems: Survey and Research Directions
Recommendations
Device-cloud Collaborative Recommendation via Meta Controller
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningOn-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based ...
Towards fair and personalized federated recommendation
AbstractRecommender systems have gained immense popularity in recent years for predicting users’ interests by learning embeddings. The majority of existing recommendation approaches, represented by graph neural network-based recommendation algorithms, ...
Highlights- We design a novel federated recommendation framework, which takes fairness and personalization of recommendation into consideration simultaneously.
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A Survey of Long-Tail Item Recommendation Methods
Recommender systems represent a critical field of AI technology applications. The core function of a recommender system is to recommend items of interest to users, but if it is only user history-based (purchasing or browsing data), it can only recommend ...
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Publication History
Online AM: 06 September 2024
Accepted: 24 August 2024
Revised: 28 June 2024
Received: 07 February 2024
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