Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Zhu, Xiaoke; Jing, Xiao‐Yuan; Wu, Di; He, Zhenyu; Cao, Jicheng; Yue, Dong; Wang, Lina

doi:10.1109/tsc.2018.2839741

Cited by 55 publications

(38 citation statements)

References 39 publications

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“…Although they are different from each other in objective functions or learning algorithms, they all adopt an L 2 norm-oriented Loss that is highly sensitive to outliers [25,27,28]. To make an LF model less sensitive to outlier data, Zhu et al propose to adopt an L 1 -norm-oriented Loss [24]. However, an LF model with an L 1 norm-oriented Loss has possibly multiple solution spaces because L 1 norm is less smooth than L 2 norm.…”

Section: B Related Workmentioning

confidence: 99%

“…Fig . 1 illustrates the differences between L 1 norm-oriented and L 2 norm-oriented Losses: a) The former is less sensitive to outliers than the latter, thereby enhancing the robustness of a resultant model [22][23][24][25] as shown in Fig. 1(a); and b) The latter is smoother than the former when the predictions and ground truth data are close, thereby enhancing the stability of a resultant model [26] as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

Shang

Luo

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

107

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A recommender system is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a recommender system. However, current LF models mostly adopt single distance-oriented Loss like an L 2 norm-oriented one, which ignores target data's characteristics described by other metrics like an L 1 norm-oriented one. To investigate this issue, this paper proposes an L 1 -and-L 2 -norm-oriented Latent Factor (L 3 F) model. It adopts two-fold ideas: a) aggregating L 1 norm's robustness and L 2 norm's stability to form its Loss, and b) adaptively adjusting weights of L 1 and L 2 norms in its Loss. By doing so, it achieves fine aggregation effects with L 1 norm-oriented Loss's robustness and L 2 norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L 3 F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

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Section: B Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

Shang

Luo

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

107

View full text Add to dashboard Cite

show abstract

“…On the other hand, the model-based CF [13][14] is another popular method for QoS prediction, which builds a model based on users' historical QoS performance to predict the unknown Qos values. Considering the problem of data sparsity, [15] proposed a matrix factorization model that integrates QoS time series, and [16] proposed a Locationaware Low-rank Matrix Factorization for QoS prediction.…”

Section: Introductionmentioning

confidence: 99%

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

Wang

Zhang

et al. 2020

IEEE Access

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As the emergence of numerous services with similar functions, it is very helpful to recommend personalized services for users, and urgent to accurately predict the QoS(Quality-of-Service) values of Web services. Collaborative Filtering (CF) is a commonly-used method to handle above issues. However, it faces two common issues: data sparsity problem and trustworthiness issue, which greatly reduces its prediction accuracy. To address this problem properly and systematically, we introduce the network embedding learning into the QoS prediction process and propose an improved QoS prediction method based on the reputation-aware network embedding learning. Firstly, a two-phase K-means clustering is adopted to filter untrustworthy users. Next, the reputation of trustworthy users is calculated, and an attributed user-service bipartite network is constructed between trustworthy users and services while considering the user reputation. Then the reputation-aware network embedding is adopted to learn the hidden representations of users. Finally, user-based CF is adopted to predict the unknown QoS values. The experimental results show that our method has a significant improvement in accuracy compared with other methods.

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“…The multimedia packets need correctness, real time data, and sequence in order to maintain streaming quality as compared to general services [15], [16]. The service quality for multimedia applications declines while meeting the QoS demands due to mobility and bandwidth variations.…”

Section: Introductionmentioning

confidence: 99%

High Quality Network and Device Aware Multimedia Content Delivery for Mobile Cloud

Saleem¹

2019

KSII TIIS

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The use of mobile devices is increasing in multimedia applications. The multimedia contents are delivered to mobile users over heterogeneous networks. Due to fluctuation in bandwidth and user mobility, the service providers are facing difficulties in providing Quality of Service (QoS) guaranteed delivery for multimedia applications. Multimedia applications depend on QoS parameters such as delay, bandwidth, and jitter to offer better user experience. The existing schemes use the single source and multisource delivery but are unable to balance between stream quality and network congestion for mobile users. We proposed a Quality Oriented Multimedia Content Delivery Scheme (QOMCDS) for the mobile cloud to deliver better quality multimedia contents for the mobile user. The multimedia contents are delivered to the mobile device based on the device's parameters and network environment. The objective video quality assessment models like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Video Quality Measurement (VQM) are used to measure the quality of the video. The client side Quality of Experience metric such as Startup delay, Rebuffering events, and Bitrate switch count was used for evaluation. The proposed scheme is evaluated using dash.js and is compared to existing schemes. The results show significant improvement over existing multimedia content delivery schemes.

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Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Cited by 55 publications

References 39 publications

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

High Quality Network and Device Aware Multimedia Content Delivery for Mobile Cloud

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Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Cited by 55 publications

References 39 publications

An L 1-and-L 2-Norm-Oriented Latent Factor Model for Recommender Systems

An L 1-and-L 2-Norm-Oriented Latent Factor Model for Recommender Systems

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

High Quality Network and Device Aware Multimedia Content Delivery for Mobile Cloud

Contact Info

Product

Resources

About

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems