Multi-Dimensional QoS Prediction for Service Recommendations

Wang, Shangguang; Ma, You; Cheng, Bo; Yang, Fangchun; Chang, Rcc

doi:10.1109/tsc.2016.2584058

Cited by 94 publications

(36 citation statements)

References 32 publications

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“…In addition, the most important research in the field of web service recommendation is QoS prediction as well. Currently, collaborative filtering (CF)-based methods [3,10,11] are commonly used. There are two types of collaborative filtering approaches, namely model-based and neighborhood-based.…”

Section: Modelling Of Kqi Predictionmentioning

confidence: 99%

“…In [3], an integrated QoS prediction approach, which unifies the modeling of QoS data via multi-linear-algebra based concepts of tensor, has been proposed. It enables efficient web service recommendation for mobile clients via tensor decomposition and reconstruction optimization.…”

Section: Modelling Of Kqi Predictionmentioning

confidence: 99%

“…It is difficult to cope with the rapid and ever-changing network and service environments. Instead, in certain circumstance it is better to identify or even predict traffic demands, service quality and potential service degradations [1][2][3]. If the service quality…”

mentioning

confidence: 99%

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Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

Xie

Xiao

et al. 2020

Applied Sciences

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In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about service problems. With the popularity of over-the-top services, this problem has become increasingly serious. Based on the service perception data crowd-sensed from massive smartphones in the mobile network, we first investigated the application of multi-label ReliefF, a well-known method of feature selection, in determining the feature weights of the perception data and propose a unified multi-label ReliefF (UML-ReliefF) algorithm. Then a feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm is proposed for the key quality indicators (KQI) prediction, by combining the UML-ReliefF and ML-kNN together in the learning. The experimental results for web browsing service show that UML-ReliefF can effectively identify the most influential features of the data and thus, lead to better performance for KQI prediction. The experiments also show that the feature-weighted KQI prediction is superior to its unweighted counterpart, since the former takes full advantage of all the features in the learning. Although there is still much room of improvement in the precision of the prediction, the proposed method is highly potential for network engineers to find the deterioration of service quality promptly and take measures before it is too late.

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Section: Modelling Of Kqi Predictionmentioning

confidence: 99%

Section: Modelling Of Kqi Predictionmentioning

confidence: 99%

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Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

Xie

Xiao

et al. 2020

Applied Sciences

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“…And it includes user-based collaborative filtering [14,22,23], service-based collaborative filtering [24][25][26][27][28], and its fusion [18]. User-based collaborative filtering is to predict the rating of users based on the ratings of their similar users, and service-based collaborative filtering is to predict the rating of users based on the ratings of services which are similar to the services chosen by the users.…”

Section: Related Workmentioning

confidence: 99%

“…With the increasing number of edge services, there are too much choices for users to meet their requirements [3,4]; therefore, the service recommendation technology is needed to help people find the optimal edge services from a huge mass of services. Because of the ubiquitous services, service recommendation is playing an increasingly significant role in our diary life [5,6]; for example, Amazon has deployed its recommending system to help recommending books and other products to its users [7].…”

Section: Introductionmentioning

confidence: 99%

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Guo

2018

Scientific Programming

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In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

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Singular value decomposition‐based behavior‐aware cloud service application programming interfaces recommendation for large‐scale software cloud directory platforms

Wang

Zhang

Zheng

et al. 2022

Concurrency and Computation

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With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large-scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform.Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self-description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD-APIR, a singular value decomposition (SVD)-based behavior-aware cloud service APIs recommendation approach for large-scale software cloud directory platforms. In SVD-APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS-Dream dataset demonstrate the effectiveness and efficiency of the proposed approach.

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Multi-Dimensional QoS Prediction for Service Recommendations

Cited by 94 publications

References 32 publications

Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Singular value decomposition‐based behavior‐aware cloud service application programming interfaces recommendation for large‐scale software cloud directory platforms

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