research-article

A deep learning approach for web service interactions

Authors:

Brahim Medjahed,

Faisal Binzagr,

Youcef AkloufAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 848 - 854

https://doi.org/10.1145/3106426.3106492

Published: 23 August 2017 Publication History

Abstract

Predicting Web service interactions such as composition and substitution provides support for developers during mashup design. In this paper, we propose a deep-learning approach for predicting compositions and substitutions. To the best of our knowledge, this work is the first to adopt deep learning for interactions prediction. We use stacked autoencoders to learn latent service features. A deep feed forward neural network leverages the learned features and the history of previous interactions to predict new ones. We conducted extensive experiments on real-world Web services to illustrate the performance of our approach. We show that the use of deep learning achieves a high accuracy level and outperforms existing models such as multi-layer perceptron and support vector machine.

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Cited By

Zhang JMa WZhang EXia X(2024)Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service RecommendationSensors10.3390/s2404118524:4(1185)Online publication date: 11-Feb-2024
https://doi.org/10.3390/s24041185
Alhosaini HAlharbi SWang XXu G(2023)API Recommendation For Mashup Creation: A Comprehensive SurveyThe Computer Journal10.1093/comjnl/bxad11267:5(1920-1940)Online publication date: 30-Nov-2023
https://doi.org/10.1093/comjnl/bxad112
Jacoby DPreston NMalhotra MCoady Y(2018)Web Services for Emergencies: Multi-Transport, Multi-Cloud, Multi-Role2018 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2018.00054(331-334)Online publication date: Jul-2018
https://doi.org/10.1109/ICWS.2018.00054

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Published In

cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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WI '17

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SIGAI
TCII

WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Zhang JMa WZhang EXia X(2024)Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service RecommendationSensors10.3390/s2404118524:4(1185)Online publication date: 11-Feb-2024
https://doi.org/10.3390/s24041185
Alhosaini HAlharbi SWang XXu G(2023)API Recommendation For Mashup Creation: A Comprehensive SurveyThe Computer Journal10.1093/comjnl/bxad11267:5(1920-1940)Online publication date: 30-Nov-2023
https://doi.org/10.1093/comjnl/bxad112
Jacoby DPreston NMalhotra MCoady Y(2018)Web Services for Emergencies: Multi-Transport, Multi-Cloud, Multi-Role2018 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2018.00054(331-334)Online publication date: Jul-2018
https://doi.org/10.1109/ICWS.2018.00054

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