CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness

Fan, Xiaoliang; Hu, Yakun; Zheng, Zibin; Wang, Yujie; Brézillon, Patrick; Chen, Wenbo

doi:10.1109/tsc.2017.2782793

Cited by 21 publications

(11 citation statements)

References 48 publications

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“…Fan et al. [23] considered spatial-temporal information and proposed context-aware service recommendations based on spatial-temporal effectiveness. Some researchers consider the user’s reputation.…”

Section: Related Workmentioning

confidence: 99%

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

Cai

2019

Sensors

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Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

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Section: Related Workmentioning

confidence: 99%

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

Cai

2019

Sensors

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“…Memory‐based CF is used to exploit full data or a sample of it in order to generate a suitable forecast . This sub‐class is mainly characterized by its high effectiveness, in addition to its simplicity of implementation .…”

Section: Related Workmentioning

confidence: 99%

“…Memory-based CF is used to exploit full data or a sample of it in order to generate a suitable forecast. 8,9,12,13,18,26,27,31,32,[37][38][39][40][41] This sub-class is mainly characterized by its high effectiveness, in addition to its simplicity of implementation. 8 The new introduced services should follow a prediction process based on the similarities between users in order to make sure that they would correspond to the active user's preferences.…”

Section: Time-aware Collaborative Filteringmentioning

confidence: 99%

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

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“…As a result, ignoring the appropriate time span may lead to inaccurate recommendations. Fan et al tried to solve this problem by measuring the weighted temporal effectiveness of differential QoS values in similarity computation. Well‐known similarity measures such as Pearson Correlation Coefficient and its variants are used in categorical TARS.…”

Section: Time Information In Service Recommendation Systemsmentioning

confidence: 99%

“…Fan et al address the problem of temporal‐spatial correlations in context‐aware service recommendation. The authors start by modeling the effectiveness of spatial correlations between the user's location and the service's location on user preference expansion before computing the similarity between users.…”

Section: Time‐aware Recommendation Approachesmentioning

confidence: 99%

Time‐aware service recommendation: Taxonomy, review, and challenges

Mezni

Fayala²

2018

Softw Pract Exp

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Summary Nowadays, a huge number of available Web services offer the same functionalities and a high quality of service, which makes the selection of suitable services a difficult task. In such situation, the services must be differentiated by additional criteria such as users' ratings. To meet this goal, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, whereas in the real world, the perception and popularity of Web services may continually change, and users' preferences and habits also shift frequently. Time is becoming an increasingly important factor in recommender systems, since time effects influence users' preferences to a large extent. In addition, quality‐of‐service performance of Web services is strongly linked to the service status and network environments, which are variable against time. Recently, a wide range of service recommendation approaches, dealing with the time dimension in user modeling and recommendation strategies, have been proposed. Thus, the purpose of this survey is to present a comprehensive study and analysis of the state‐of‐the‐art on time‐aware service recommendation. We identify the techniques used in recommender systems to provide the best services. Moreover, we present a classification of time‐aware recommender systems based on the target recommendation time, the type of relationship between users, and the type of feedback. Besides, we present a comparison between time‐aware recommendation approaches, and we discuss their advantages and disadvantages. Finally, challenges and requirements of time‐aware service recommendation as well as the future directions are identified according to the studied approaches.

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CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness

Cited by 21 publications

References 48 publications

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Time‐aware service recommendation: Taxonomy, review, and challenges

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