Web service recommendation method based on improved deep learning

A technology of web service and recommendation method, applied in the field of deep learning, which can solve the problem that the user's cold start does not take into account the user's service functional preference and quality preference at the same time.

Pending Publication Date: 2019-10-15
电子科技大学成都学院
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Problems solved by technology

However, the traditional method is extremely sensitive to the sparsity problem, and there is a user cold start problem,

Method used

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  • Web service recommendation method based on improved deep learning
  • Web service recommendation method based on improved deep learning
  • Web service recommendation method based on improved deep learning

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Experimental program
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Embodiment Construction

[0037] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0038] Such as figure 1 As shown, in order to verify the effectiveness of the algorithm, the algorithm uses the WSD REAM data set released by the Chinese University of Hong Kong [15] , the dataset collects actual QoS measurements including response time and throughput obtained from 339 users on 5,825 Web services, in addition to information on 339 users and 5,825 Web services.

[0039] The web service-free text tag data in the experimental data is obtained by extracting word tags from the WSDL (WebServices Description Language) document in the web service information. WSDL is a kind of document written in XML that can describe a certain Web service. It can specify the location of the service, and the operations (or methods) provided by thi...

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Abstract

The invention discloses a web service recommendation method based on improved deep learning. The web service recommendation method comprises the following steps: extracting free text label data from aWSDL document of a web service; obtaining a user-scoring matrix from the free text label data of the web service by utilizing the LFM based on the SDAE; constructing a functional feature preference matrix of the user-service according to the user-scoring matrix, and constructing a mixed preference matrix in combination with the QoS data set of the web service; and predicting a missing QoS value of the mixed preference matrix by using a collaborative filtering algorithm based on the mixed preference matrix to realize intelligent recommendation. According to the invention, the web service recommendation method based on improved deep learning can be realized.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to a web service recommendation method based on improved deep learning. Background technique [0002] With the development of Internet technology and the advent of web2.0, the number of web services is increasing rapidly. Among the huge number of web services, it is difficult for users to choose services that meet their own functional attributes and high-quality services. Therefore, recommending such services for users has It has become a popular research direction in the field of service computing. [0003] Traditional QoS-based collaborative filtering algorithms [1-3] are mostly based on user QoS call information, by calculating user similarity or service similarity, and selecting similar neighbors, predicting missing QoS values, and recommending them for users . However, the traditional methods are extremely sensitive to the sparsity problem, and there is a problem of user cold sta...

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Application Information

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IPC IPC(8): G06F16/9535G06F17/16
CPCG06F16/9535G06F17/16
Inventor 邹倩颖陈东祥刘家委李雪
Owner 电子科技大学成都学院
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