Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Li, Yan; Guo, Yan

doi:10.1155/2018/2181974

Cited by 8 publications

(7 citation statements)

References 27 publications

(27 reference statements)

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“…Hence, it is important and necessary to research the new architecture of web services, on the combinations with other good techniques, and the integration of services [71]. To improve accuracy expression of user preference, the study in [72] proposes a cultural distance-aware service recommendation algorithm utilizing joint factors of similarity and the local characteristics of users. They consider that users may be similar behavior in the same edge server and different users in other edge servers.…”

Section: Web Service Researchmentioning

confidence: 99%

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Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun

et al. 2020

IEEE Access

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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.

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Section: Web Service Researchmentioning

confidence: 99%

“…In [72], Wu et al introduce a cultural distance-aware service recommendation approach to improve accuracy expression of user preference. The conventional CF only focuses on the similarity of the rating matrix, which is not suitable for services in the edge network environment.…”

Section: A Optimization Of Recommender Systemsmentioning

confidence: 99%

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun

et al. 2020

IEEE Access

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“…Many literature over the past decade focused on developing service recommendation systems. Most of these works focus on collaborative filtering based recommendation [6,9,10], content-based recommendation [11] and model-based recommendation [12,13].…”

Section: Related Workmentioning

confidence: 99%

“…where S denotes the set of similar servers to server s, who have provided service j; nr s ,j is the value of service j provided by server s in the row-normal matrix SS nu ; r smin and r smax are the lowest and the highest values from server s in the original matrix SS, respectively; and Sim(u, u ) can be computed by formula (10).…”

Section: Location-based Collaborative Filteringmentioning

confidence: 99%

Mobility-Aware Personalized Service Recommendation in Mobile Edge Computing

Hong-xia

Dong

Yang

2020

Preprint

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With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users\' mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation approach based on user trajectory and QoS predictions. In the proposed method, users' trajectory is firstly discovered by hybrid long-short memory networks. Then, given users\' trajectories, service QoSs are predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy based on the aforementioned information. Experimental results based on the real base station dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of the accuracy in mobile edge computing.

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“…There are various reasons for this problem. For example, users' privacy concerns may cause anxiety, thereby limiting the amount of shared information; each user may only view a small number of items, which yields limited useful data for similarity computing in personalization services; and data for new users are very limited, which leaves the personalization service with a "cold start" problem [28]. To alleviate data sparsity, one solution is to integrate the data and knowledge using cross-edge rather than single-edge solutions.…”

Section: Single-edge Versusmentioning

confidence: 99%

CEPTM: A Cross‐Edge Model for Diverse Personalization Service and Topic Migration in MEC

Zhang

Cui

et al. 2018

Wireless Communications and Mobile Computing

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For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single-edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

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Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Cited by 8 publications

References 27 publications

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Mobility-Aware Personalized Service Recommendation in Mobile Edge Computing

CEPTM: A Cross‐Edge Model for Diverse Personalization Service and Topic Migration in MEC

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