Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition

Cheng, Tian; Wen, Junhao; Xiong, Qingyu; Zeng, Jun; Zhou, Wei; Cai, Xueyuan

doi:10.1109/access.2019.2909548

Cited by 21 publications

(9 citation statements)

References 20 publications

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“…Although many studies have improved the prediction accuracy from a series of aspects, the existing methods have severe limitations and can only extract or learn shallow features. Chen et al [21] proposed a web service recommendation method based on QoS prediction and hierarchical tensor decomposition . The method is called QoSHTD, which is based on location clustering and hierarchical tensor decomposition.…”

Section: Related Workmentioning

confidence: 99%

A Two-Tier Service Filtering Model for Web Service QoS Prediction

Zhang

2020

IEEE Access

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Section: Related Workmentioning

confidence: 99%

A Two-Tier Service Filtering Model for Web Service QoS Prediction

Zhang

2020

IEEE Access

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“…QoS prediction models are developing rapidly towards selecting candidate services [1][2][3][4][5][6]. For example, [1,3] use a model-based Collaborative Filtering (CF) method to predict the QoS values of atomic service.…”

Section: Introductionmentioning

confidence: 99%

An expanded Matrix Factorization model for real-time Web service QoS prediction

2021

KSII TIIS

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Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.

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“…Collaborative Filtering (CF) is one of the most widely used methods for QoS prediction [2], especially neighborhoodbased CF [3][4][5][6][7][8][9][10][11]. The key step of CF is to calculate the similarity between users (or services), and then predict the unknown QoS based on the historical QoS values provided by similar users (or services).…”

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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Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition

Cited by 21 publications

References 20 publications

A Two-Tier Service Filtering Model for Web Service QoS Prediction

A Two-Tier Service Filtering Model for Web Service QoS Prediction

An expanded Matrix Factorization model for real-time Web service QoS prediction

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

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