A time-aware Bayesian approach for optimal manufacturing service recommendation in distributed manufacturing environments

Zhang, S.; Zhang, W.Y.; Liu, J.; Wu, Jian

doi:10.1016/j.jmsy.2012.07.012

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…When a user deals with hard decision making and looks for selecting an item from a long list, he recourses to his related friends for recommending him the best one . In this context, integrating social networks could offer results with more accuracy .…”

Section: Related Workmentioning

confidence: 99%

“…When a user deals with hard decision making and looks for selecting an item from a long list, he recourses to his related friends for recommending him the best one. 1,44,45 In this context, integrating social networks could offer results with more accuracy. 15 However, because of the existence of dishonest users which could exaggerate their partners' services or badmouth their competitors' services, problems of data credibility and QoS reliability have appeared.…”

Section: Trust-based Time-aware Recommender Systemsmentioning

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: Related Workmentioning

confidence: 99%

Section: Trust-based Time-aware Recommender Systemsmentioning

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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“…This can be explained by their recent appearance compared to Web services. Other works consider the general aspect of services in the recommendation process …”

Section: Comparison Of Time‐aware Recommendation Approachesmentioning

confidence: 99%

“…Other works consider the general aspect of services in the recommendation process. 13,29,53 As shown in Figure 6, it is very obvious that most recommendation approaches 15,20,53 take into account the implicit feedback. This choice can be explained by the ease of manipulating the implicit feedback compared with other types of feedback.…”

Section: Comparison Of 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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“…These criteria were assessed by different numerical terms. In the manufacturing service domain, our previous work 32 proposed a set of QoS properties used for optimal manufacturing service recommendation, such as service and time cost, availability, performance, integrity, interoperability, security, etc. Considering the actual situation, we should select different QoS parameters according to different evaluation objects.…”

Section: Assessing Data Preparationmentioning

confidence: 99%

A novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory

Zhang

2016

Journal of Algorithms & Computational Technology

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The new era of global cooperation and competition has created challenges for manufacturing enterprises in selecting optimal services and collaboration enterprises. Multi-criteria decision making (MCDM) and evaluation has been applied in the domain of manufacturing service to address these issues. This study proposes a novel method for MCDM and evaluation of manufacturing services using collaborative filtering and interval-valued intuitionistic fuzzy (IVIF) theory. Quality of service (QoS)-aware collaborative filtering predicts missing QoS values in an IVIF rating matrix that is normalized from initial matrices that employ mixed numerical terms. An interval-valued intuitionistic fuzzy weighted arithmetic (IIFWA) aggregation operator derived from IVIF theory is utilized to evaluate and select the optimal service(s) or supplier(s). An illustrative example of manufacturing service evaluation and comparison are provided to validate the effectiveness and practicability of the proposed method.

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A time-aware Bayesian approach for optimal manufacturing service recommendation in distributed manufacturing environments

Cited by 15 publications

References 12 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 novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory

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