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Web services recommendation leveraging semantic similarity computing

  • * Corresponding author: Zhangbing Zhou

    * Corresponding author: Zhangbing Zhou
The first author is supported by the National Natural Science Foundation of China (No. 61379126 and 61662021).
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  • With the popularity of Web services adopted for supporting domain applications, recommending and composing appropriate services with respect to user requirements is a challenge. This paper proposes a dynamic programming and variable length genetic algorithm for the recommendation and composition of Web services. Generally, starting and ending services are determined leveraging the constructed service network model. Based on which, services are selected and composed, such that these services should be more appropriate on satisfying users' requirements. Experimental evaluation result shows that our technique is effective and can improve the accuracy of service recommendation.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.


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  • Figure 1.  The process of similarity computing. The similarity calculation is mainly based on some semantic computing methods

    Figure 2.  The simplified web service network model based on spatial Web services in CSISS OWSs, which consists of 6 operations and 36 directed edges

    Figure 3.  State change of Dynamic Programming, the state will change until all the operations are included, and only part of the steps are shown in the diagram

    Figure 4.  The computation process of cross operator.In order to simplify, this graph is the first calculation process, the subsequent variable length operator will make the length of the chromosome change, the length will become uncertain

    Figure 5.  The process of decreasing the length in variable-length operator. We decrease the length of subsegments by deleting some non-critical operations randomly

    Figure 6.  The process of increasing the length in variable-length operator. We increase the length of subsegments by adding some non-critical operations randomly

    Figure 7.  The run time and the aim function of DP algorithm when the length of paths is set from 3 to 9

    Figure 8.  The average weight of DP algorithm when the length of paths is set from 3 to 9

    Figure 9.  Comparison of run time for DP and GA, when the number of operations is set from 30 to 70

    Figure 10.  Comparison of aim function for DP and GA, when the number of operations is set from 30 to 70

    Figure 11.  Comparison of average weight for DP and GA, when the number of operations is set from 30 to 70

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