文件名称:web-rank
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Abstract—Service-oriented computing and Web services are
becoming more and more popular, enabling organizations to use
the Web as a market for selling their own Web services and consuming
existing Web services others. Nevertheless, with the
increasing adoption and presence of Web services, it becomes
more difficult to find the most appropriate Web service that satisfies
both users’ functional and nonfunctional requirements. In
this paper, we propose an effective Web service ranking approach
based on collaborative filtering (CF) by exploring the user behavior,
in which the invocation and query history are used to infer
the potential user behavior. CF-based user similarity is calculated
through similar invocations and similar queries (including
functional query and QoS query) between users.-Abstract—Service-oriented computing and Web services are
becoming more and more popular, enabling organizations to use
the Web as a market for selling their own Web services and consuming
existing Web services others. Nevertheless, with the
increasing adoption and presence of Web services, it becomes
more difficult to find the most appropriate Web service that satisfies
both users’ functional and nonfunctional requirements. In
this paper, we propose an effective Web service ranking approach
based on collaborative filtering (CF) by exploring the user behavior,
in which the invocation and query history are used to infer
the potential user behavior. CF-based user similarity is calculated
through similar invocations and similar queries (including
functional query and QoS query) between users.
becoming more and more popular, enabling organizations to use
the Web as a market for selling their own Web services and consuming
existing Web services others. Nevertheless, with the
increasing adoption and presence of Web services, it becomes
more difficult to find the most appropriate Web service that satisfies
both users’ functional and nonfunctional requirements. In
this paper, we propose an effective Web service ranking approach
based on collaborative filtering (CF) by exploring the user behavior,
in which the invocation and query history are used to infer
the potential user behavior. CF-based user similarity is calculated
through similar invocations and similar queries (including
functional query and QoS query) between users.-Abstract—Service-oriented computing and Web services are
becoming more and more popular, enabling organizations to use
the Web as a market for selling their own Web services and consuming
existing Web services others. Nevertheless, with the
increasing adoption and presence of Web services, it becomes
more difficult to find the most appropriate Web service that satisfies
both users’ functional and nonfunctional requirements. In
this paper, we propose an effective Web service ranking approach
based on collaborative filtering (CF) by exploring the user behavior,
in which the invocation and query history are used to infer
the potential user behavior. CF-based user similarity is calculated
through similar invocations and similar queries (including
functional query and QoS query) between users.
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web rank.pdf