Web Service Recommendation Based on Time Series Forecasting and Collaborative Filtering

Hu, Yan; Peng, Qimin; Hu, Xiaohui; Yang, Rong

doi:10.1109/icws.2015.40

Cited by 44 publications

(28 citation statements)

References 19 publications

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“…The shifting of service bias is the most used time effect in this category of TARS. Moreover, most continuous time‐aware approaches adopt either collaborative filtering or hybrid filtering …”

Section: Time Information In Service Recommendationmentioning

confidence: 99%

“…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%

“…Moreover, a lot of approaches deal only with one‐time effect (eg, shifting of service bias) and ignore the other effects. This fact can influence the accuracy of the prediction process . Most approaches do not correctly incorporate and process service‐related time information.…”

Section: Introductionmentioning

confidence: 99%

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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.

show abstract

Section: Time Information In Service Recommendationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Fayala¹,

Mezni

2019

Concurrency and Computation

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show abstract

“…Shifting of service bias is the most used time effect in this category of TARS. Moreover, most continuous time‐aware approaches adopt either collaborative filtering or hybrid filtering …”

Section: Time Information In Service Recommendation Systemsmentioning

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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“…Li et al [37] presented a comprehensive QoS prediction framework for composite services, which employs the ARMA model to predict the future QoS of individual service. Hu et al [38] presented a novel personalized QoS prediction approach, which integrates the time series-based QoS forecasting for individual service. Ye et al [39] proposed a novel approach to select and compose cloud services from a long-term and economic model-driven perspective, which uses QoS history value to predict the long-term QoS value.…”

Section: Related Workmentioning

confidence: 99%

Time-Aware Service Ranking Prediction in the Internet of Things Environment

Huang

Cheng

et al. 2017

Sensors

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With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

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Web Service Recommendation Based on Time Series Forecasting and Collaborative Filtering

Cited by 44 publications

References 19 publications

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

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

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

Time-Aware Service Ranking Prediction in the Internet of Things Environment

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