An Approach of RGPS-Guided On-Demand Service Organization and Recommendation

Liu, Jianxiao; He, Keqing; Wang, Jian; Yu, Dunhui; Feng, Zaiwen; Ning, De; Zhang, Xiuwei

doi:10.3724/sp.j.1016.2013.00238

Cited by 8 publications

(5 citation statements)

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“…It can be formalized as follows: 1 ) =<S 1 ={ActorTracker,www.actortracker.com}, S r ={movie lover, celebrity news lover, e-shopper}, S g ={find upcoming show, find upcoming movie, find celebrity news, learn e-shopping experience}, S p ={ }, S t ={movie, video, television, shopping, celebrity}> In this paper, our basic hypothesis is that, enough annotation knowledge is registered in the service repositories. How to get these attribute is not the point in this paper and it can be found in our previous work [2]. SDP can be encoded as a XML document (see Figure 3).…”

Section: A User Interests Profilementioning

confidence: 98%

“…We assume that service annotation data has been produced by domain expert and registered into service repository in accordance with MFI-7. In service repository, SDP is represented with four two-dimensional matrices: service-role matrix m denotes the total number of roles which are related to all services in repository, 2 m denotes the total number of goals which are related to all services, 3 m denotes the total number of processes which are related to services and 4 m denotes the total number of tags which are related to services. Here we take service-role matrix 1 n m SR A u as example and demonstrate it in Table I.…”

Section: Services Repositorymentioning

confidence: 99%

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Interest-Driven Web Service Recommendation Based on MFI-7

Zhang

Wang

et al. 2013

2013 IEEE International Conference on Services Computing

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Composing and reusing services is the main advantage of Service Oriented Software Engineering (SOSE). Faced with the large amount of Web services which are available on World Wide Web, how to select and recommend suitable Web services becomes a key issue in service computing. The most popular service recommendation technique is QoS-based Collaborate Filtering (CF) with user-service QoS matrix. However, it cannot well capture the new interests of users and handle the cold start problem. In this paper we propose an interest-driven recommendation approach which leverage User Interest Profile (UIP) to represent users' interests. UIP is generated in accordance with an ISO standard named as MFI-7. The similarity between UIPs is used to produceusers' nearest neighbors. The Top-K recommendations will be generated from these neighbors' mostly-used services. To show the effectiveness of our approach, a developed Web service registry and repository platform is used as a testbed to produce preliminary evaluation results.

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Section: A User Interests Profilementioning

confidence: 98%

Section: Services Repositorymentioning

confidence: 99%

Interest-Driven Web Service Recommendation Based on MFI-7

Zhang

Wang

et al. 2013

2013 IEEE International Conference on Services Computing

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“…The service clusters are mainly organized from aspects of service execution capability and information interaction between services. And we also have organized service clusters in the consideration of users' features, like users' roles and goals [5]. Sellami et al have used community to organize and manage Web services [6] [7].…”

Section: Related Workmentioning

confidence: 99%

Web Service Aggregation Platform Implementation Based on Join Operation

Liu¹,

Ning²,

Xing³

et al. 2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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In the era of service computing, it needs to develop web service aggregation platform to organize and manage services, and thus to help users to discover the atomic and a set of services with correlations to meet their functional and non-functional requirements. This is an important research point in the service-oriented software engineering. This paper develops a web service aggregation platform based on the join operation in relational database (RDB). Firstly, it uses the self-join operation in RDB to cluster service to form different service clusters. Then it organizes different service clusters in further according to the service execution dependency relationship, and thus to realize service aggregation. Finally, the service selection function is implemented to meet users' personal requirements.

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“…Based on the fact that users usually put forth a coarse-granularity request. [11] proposed an on-demand service organization and recommendation method based on role, goal, process and service model for a domain problem to satisfy user's personalized and diverse requirements. [12] proposed an event-based web service composition in order to satisfy user's multiple requirements and be realized easily.…”

Section: Related Workmentioning

confidence: 99%