Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

Shen, Limin; Pan, Maosheng; Liu, Linlin; You, Dianlong; Li, Feng; Chen, Zhen

doi:10.1109/access.2020.3022891

Cited by 14 publications

(12 citation statements)

References 43 publications

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“…As an enhancement of traditional MF, regularization has been incorporated further [24]- [27]. However, the prediction accuracy is yet to achieve a satisfactory level, since MFbased methods are unable to capture the higher-order, nonlinear relationship between users and services in terms of QoS values [9]. Due to the online training, in many cases, MF-based methods experience higher prediction time, and thereby, barely suit for a real-time system.…”

Section: Model-based Cf (Matrix Factorization)mentioning

confidence: 99%

“…Model-based CF (Factorization Machine): Factorization Machine (FM) usually casts the QoS prediction to a regression problem. In general, FM performs better than MF in terms of prediction accuracy, since most of the time, it can capture the non-linearity between QoS values of services invoked by different users [9], [38], [39]. Some deep learning-based methods also have been explored for QoS prediction in the literature.…”

Section: Model-based Cf (Matrix Factorization)mentioning

confidence: 99%

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FES: A Fast Efficient Scalable QoS Prediction Framework

Chattopadhyay¹,

Adak²,

Chowdhury³

2021

Preprint

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Quality-of-Service (QoS) prediction of web service is an integral part of services computing due to its diverse applications in service composition/selection/recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm should be faster in terms of prediction time to be compatible with a real-time system. The other important factor to consider is scalability to tackle large-scale datasets. The existing QoS prediction algorithms often satisfy one goal while compromising the others. In this paper, we propose a semi-offline QoS prediction framework to achieve three important goals together: higher accuracy, faster prediction time, and scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework (FES) consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the proposed pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on WS-DREAM datasets show the efficiency (in terms of accuracy), scalability, and fast responsiveness (in terms of prediction time) of FES as compared to the state-of-the-art methods.

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Section: Model-based Cf (Matrix Factorization)mentioning

confidence: 99%

Section: Model-based Cf (Matrix Factorization)mentioning

confidence: 99%

FES: A Fast Efficient Scalable QoS Prediction Framework

Chattopadhyay¹,

Adak²,

Chowdhury³

2021

Preprint

View full text Add to dashboard Cite

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“…And, the widely adopted similarity computational model includes Pearson correlation coefficient, cosine. etc [27]. 2) Neighborhood Selection.…”

Section: Introductionmentioning

confidence: 99%

“…2) Neighborhood Selection. In this step, similar neighbors of users and services are identified based on the computed similarities [27]. 3) Collaborative Prediction.…”

Section: Introductionmentioning

confidence: 99%

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QoS prediction for service selection and recommendation with a deep latent features autoencoder

Zohra

Benmerzoug

2022

ComSIS

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The number of services on the Internet has increased rapidly in recent years. This makes it increasingly difficult for users to find the right services from a large number of the functionally equivalent candidate. In many cases, the number of services invoked by a user is quite limited, resulting in a large number of miss ing QoS values and sparseness of data. Consequently, predicting QoS values of the services is important for users to find the exact service among many functionally similar services. However, improving the accuracy of QoS prediction is still a prob lem. Despite the successful results of the proposed QoS prediction methods, there are still a set of issues that should be addressed, such as Sparsity and Overfitting. T address these issues and improve prediction accuracy. In this paper, we propose a novel framework for predicting QoS values and reduce prediction error. This frame work named auto-encoder for neighbor features (Auto-NF) consists of three steps. In the first step, we propose an extended similarity computation method based on Euclidean distance to compute the similarity between users and find similar neigh bors. In the second step, we form clusters of similar neighbors and partition the initial matrix into sub-matrices based on these clusters to reduce the data sparsity problem. In the third step, we propose a simple neural network autoencoder that can learn deep features and select an ideal number of latent factors to reduce the over fitting phenomenon. To validate and evaluate our method, we conduct a series of experiments use a real QoS dataset with different data densities. The experimental results demonstrate that our method achieves higher prediction accuracy compared to existing methods.

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TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based Learning

Kumar

Chattopadhyay

2022

Service-Oriented Computing

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In the realm of modern service-oriented architecture, ensuring Quality of Service (QoS) is of paramount importance. The ability to predict QoS values in advance empowers users to make informed decisions, ensuring that the chosen service aligns with their expectations. This harmonizes seamlessly with the core objective of service recommendation, which is to adeptly steer users towards services tailored to their distinct requirements and preferences. However, achieving accurate and real-time QoS predictions in the presence of various issues and anomalies, including outliers, data sparsity, grey sheep instances, and cold start scenarios, remains a challenge. Current state-of-the-art methods often fall short when addressing these issues simultaneously, resulting in performance degradation. In response, in this paper, we introduce an anomaly-resilient real-time QoS prediction framework (called ARRQP). Our primary contributions encompass proposing an innovative approach to QoS prediction aimed at enhancing prediction accuracy, with a specific emphasis on improving resilience to anomalies in the data. ARRQP utilizes the power of graph convolution techniques, a powerful tool in graph-based machine learning, to capture intricate relationships and dependencies among users and services. By leveraging graph convolution, our framework enhances its ability to model and seize complex relationships within the data, even when the data is limited or sparse. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. By utilizing robust loss functions, this approach effectively reduces the impact of outliers during the training of the predictive model. Additionally, we introduce a method for detecting grey sheep users or services that is resilient to sparsity. These grey sheep instances are subsequently treated separately for QoS prediction. Furthermore, we address the cold start problem as a distinct challenge by emphasizing contextual features over collaborative features. This approach allows us to effectively handle situations where newly introduced users or services lack historical data. Experimental results on the publicly available benchmark WS-DREAM dataset demonstrate the framework's effectiveness in achieving accurate and timely QoS predictions, even in scenarios where anomalies abound.

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Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

Cited by 14 publications

References 43 publications

FES: A Fast Efficient Scalable QoS Prediction Framework

FES: A Fast Efficient Scalable QoS Prediction Framework

QoS prediction for service selection and recommendation with a deep latent features autoencoder

TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based Learning

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