An Embedding Based Factorization Machine Approach for Web Service QoS Prediction

Wu, Yaoming; Xie, Fei; Chen, Liang; Chen, Chuan; Zheng, Zibin

doi:10.1007/978-3-319-69035-3_19

Cited by 38 publications

(31 citation statements)

References 17 publications

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“…Luo et al [24] proposed a matrix-factorization model with Tikhonov regularization terms and under a nonnegativity constraint for QoS prediction. Wu et al [14] embedded Complexity 3 the user id and the service id to vectors and employed factorization machine to predict QoS for users.…”

Section: Collaborative Filtering Based Qos Predictionmentioning

confidence: 99%

“…Then, it combines the similar users' information and QoS records to conduct matrix factorization. Finally, it predicts QoS for users (5) EFMPred (embedding based factorization machine) [14]: this approach firstly embeds the user id and service id to vectors. Then, it uses factorization machine to predict QoS for users (6) RegionKNN (region K nearest neighbors) [27]: this approach firstly clusters users and services on the basis of the location information and QoS values.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Based on the characteristics of our QoS prediction problem and the significance of embedding and factorization machine, in this paper, we propose a Location-Aware QoS prediction approach, by exploiting Neural network and Factorization Machine (called LANFM), to improve the user-perceived experience. This paper is extended from its preliminary conference version [14], which mainly takes advantage of users' id feature and services' id feature for QoS prediction. Moreover, we only consider impact parameters like matrix density and dimension of embedding vector (matrix density is set as 10%) in the conference paper.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Quality of Service via Leveraging Location Information

et al. 2019

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QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location-aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user-perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real-world dataset to validate the effectiveness of the LANFM model.

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Section: Collaborative Filtering Based Qos Predictionmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Quality of Service via Leveraging Location Information

et al. 2019

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“…Nowadays, more and more enterprises and organizations have published their deep computing functionalities and big data to the Internet in the form of Web API driven by the API economic model, realizing a symbiosis and win-win network. As of July 2020, Programmableweb, the world's largest Web API platform, has published 23,194 Web APIs with 490 categories, and the Web APIs have increased rapidly by 30% annually in the past four years [9]. APIs.guru and RapidAPI platforms are also constantly enriching and improving their Web API resources.…”

Section: Introductionmentioning

confidence: 99%

“…In the last years, many methods are devised to improve the performance of QoS prediction, and the widely used techniques include collaborative filtering (CF) [15][16][17][18], matrix factorization (MF) [19][20][21][22], and factorization machine (FM) [23][24][25]. While achieving great success, these approaches have the following drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

Kang

Liang

Ding

et al. 2023

Engineering Reports

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With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results.

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An Embedding Based Factorization Machine Approach for Web Service QoS Prediction

Cited by 38 publications

References 17 publications

Predicting Quality of Service via Leveraging Location Information

Predicting Quality of Service via Leveraging Location Information

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

QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

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