Heuristic Based Time-Aware Service Selection Approach

Guidara, Ikbel; Guermouche, Nawal; Chaari, Tarak; Tazi, Saïd; Jmaïel, Mohamed

doi:10.1109/icws.2015.19

Cited by 19 publications

(26 citation statements)

References 17 publications

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“…Existing approaches such as content‐based, memory‐based, and trust‐based are generally continuous. Shifting of service bias and shifting of user preferences are also the most used time effects in categorical TARS.…”

Section: Time Information In Service Recommendationmentioning

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.

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Section: Time Information In Service Recommendationmentioning

confidence: 99%

“…Moreover, most approaches suffer from data sparsity and cold‐start problems. This is due to the use of collaborative filtering, like in the work of Yu and Huang and Zhang et al Other problems are related each to the used method or technique like ignoring the dependencies between QoS values in the work of Guidara et al…”

Section: Related Workmentioning

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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“…In some cases, we need simply to ask the user directly about his requirements. Therefore, explicit feedback allows users to define their requirements and all useful information in terms of time explicitly . To this fact, complex types of interaction are required to establish the user's requirements and preferences mostly because of the absence of historical information.…”

Section: Service Recommendation Based On User Feedbackmentioning

confidence: 99%

“…In some cases, using only implicit or explicit feedback could not be the best solution to satisfy all the user's requirements. As a solution to this problem, some works have adopted hybrid feedback by merging implicit and explicit feedback to eliminate their limits . For example, in order to avoid having wrong interpretations made when using implicit feedback, explicit feedback allows the user to express his needs explicitly.…”

Section: Service Recommendation Based On User Feedbackmentioning

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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“…Web service composition is the organization of those services in some order to perform a particular task. Service providers sell their dynamic web service components in the international market so that these services can be used by the customers [7]. The important property to explain a web task is that takes account of the signature, session states and the requirements of specific criteria.…”

Section: Related Workmentioning

confidence: 99%

A Web Service Composition Framework based on Functional Weight to Reach Maximum QoS

Yacoab¹,

Alameen²,

Sha³

2018

ijacsa

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Abstract-The recent trend in the web world is to accomplish almost all the user services in every field through the web portals of the respective organizations. But a specific task with series of actions cannot be completed by a single web service with limited functionality. Therefore, multiple web services with different functionalities are composed together to attain the result. Web service composition is an approach that combine various services to fulfill the Web related tasks with preferred quality. Composition of such services will become more challenging when these web services are with similar functionalities, varying Quality and from several providers. Hence, the overall QoS (Quality of Service) could be considered as the major factor for composition. Moreover, in most of the compositions the expected QoS cannot be attained when the task is finished. Sometimes the complete task may have affected by a poor performed single web service. So, while composition, at most care should be taken to select a particular web service. Composing web services dynamically is the main method used to overcome these difficulties. However, to reach the actual functionality of the specific task the quality of each individual service is very much necessary. The QoS of a web service normally evaluated using the non-functional attributes, such as response time, availability, reliability, throughput, etc. Also, while composition, the same level of quality is not expected for individual web services that are included in the chain. So, a framework proposed in this research paper, for web service composition by setting appropriate weightage for the non-functional parameters. Experimental results show that implementation of this method will definitely pave the way to reach the maximum performance of the composition with improved QoS.

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Heuristic Based Time-Aware Service Selection Approach

Cited by 19 publications

References 17 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

A Web Service Composition Framework based on Functional Weight to Reach Maximum QoS

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