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MSRDL: Deep learning framework for service recommendation in mashup creation.

Ting Yu1, Hailin Liu2, Lihua Zhang3

  • 1Jiaxing Nanhu University, Jiaxing, 314001, China. yuting_jx@163.com.

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We introduce a Deep Learning framework (MSRDL) for recommending services to developers building mashups. MSRDL effectively addresses challenges in feature utilization and text understanding, improving service recommendations.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Service-oriented computing has rapidly advanced, leading to a proliferation of services.
  • Developers face challenges selecting appropriate services for creating mashups and applications.
  • Existing service recommendation systems struggle with feature utilization and understanding textual requirements.

Purpose of the Study:

  • To propose a novel framework for mashup-oriented service recommendation.
  • To enhance the accuracy and efficiency of service recommendations for developers.
  • To address limitations in current service recommendation approaches.

Main Methods:

  • Developed the Mashup-oriented Service Recommendation framework based on Deep Learning (MSRDL).
  • Designed a content component for generating mashup and service representations.
  • Created an interaction component to model invocation records between mashups and services.
  • Integrated features using a Multi-Layer Perceptron (MLP) for service recommendation lists.

Main Results:

  • Experimental results demonstrated the effectiveness of the MSRDL framework.
  • The proposed method outperformed existing state-of-the-art techniques on ProgrammableWeb datasets.
  • MSRDL showed superiority in service recommendation accuracy.

Conclusions:

  • The MSRDL framework offers a robust solution for mashup-oriented service recommendation.
  • Deep learning techniques significantly improve feature utilization and requirement understanding.
  • This work advances the field of service recommendation systems.