Abstract
Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.
Highlights
Ubiquitous recommender systems are a vital and indispensable technology and application of Big Data and Artificial Intelligence (AI) [1]
The recommender systems start off becoming popular in various information access systems and has been successfully applied in pervasive across numerous web domains, such as e-commerce (e.g., Amazon, Netflix, Alibaba), information retrieving (e.g., Google, Yahoo, Baidu), social network
Different from [21], [31], this survey contributes on these respects: 1) we comprehensively review the state of the art literature on the convergence and investigate the holistic technical spectrum in terms of four enablers; 2) we summarize the deployment challenges of recommender systems by edge computing; 3) we discuss open issues currently limiting real-world implementations and identify the future directions along this dimension
Summary
Ubiquitous recommender systems are a vital and indispensable technology and application of Big Data and Artificial Intelligence (AI) [1]. On one hand, recommender systems expect to deeply understand users’ behaviors, demands, and interest via edge servers with the proximity of users [20], processing sparse, heterogeneity, mobility, and volatility data sources, solving cold-start, exploration and exploitation and security and privacy problem, providing perfect personalized services for users. Recommender systems are an efficient way to cache the most related and popular content at edge servers to maximize resource utilization and reduce latency by matching the requirements, hobbies, and habits according to users’ environments [30]
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