research-article

Semantic content-based recommendation of software services using context

Authors:

Nikolay MehandjievAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 7, Issue 3

Article No.: 17, Pages 1 - 20

https://doi.org/10.1145/2516633.2516639

Published: 30 September 2013 Publication History

Abstract

The current proliferation of software services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional services, yet their direct application to software services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune software services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

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Cited By

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Index Terms

Semantic content-based recommendation of software services using context
1. Information systems
  1. Information retrieval
  2. World Wide Web
    1. Web applications
    2. Web services

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 7, Issue 3

September 2013

149 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/2516633

Issue’s Table of Contents

Copyright © 2013 ACM.

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Publication History

Published: 30 September 2013

Accepted: 01 March 2013

Revised: 01 October 2012

Received: 01 February 2012

Published in TWEB Volume 7, Issue 3

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Cited By

Cai WYang MLin L(2024)An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group PreferencesSystems10.3390/systems1211049112:11(491)Online publication date: 14-Nov-2024
https://doi.org/10.3390/systems12110491
Zhou HLiao SGuo F(2024)TriGCN: Graph Convolution Network Based on Tripartite Graph for Personalized Medicine Recommendation SystemSystems10.3390/systems1210039812:10(398)Online publication date: 26-Sep-2024
https://doi.org/10.3390/systems12100398
Chen MYu JWang JLi X(2024)HTGTransactions on Emerging Telecommunications Technologies10.1002/ett.495135:3Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1002/ett.4951
Cao BPeng MZhang LQing YTang BKang GLiu J(2023)Web Service Recommendation via Integrating Heterogeneous Graph Attention Network Representation and FiBiNET Score PredictionIEEE Transactions on Services Computing10.1109/TSC.2023.328718916:5(3837-3850)Online publication date: Sep-2023
https://doi.org/10.1109/TSC.2023.3287189
Wang YChen JHuang QXia XJiang B(2023)Deep learning-based open API recommendation for Mashup developmentScience China Information Sciences10.1007/s11432-021-3531-066:7Online publication date: 12-Jun-2023
https://doi.org/10.1007/s11432-021-3531-0
Yu TYu DWang DHu X(2023)Web service recommendation for mashup creation based on graph networkThe Journal of Supercomputing10.1007/s11227-022-05011-379:8(8993-9020)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1007/s11227-022-05011-3
Ma CAn QYang ZZhang HQu J(2022)A Hybrid Recommendation Approach for Medical Services That Incorporates Knowledge GraphsProcesses10.3390/pr1008150010:8(1500)Online publication date: 29-Jul-2022
https://doi.org/10.3390/pr10081500
Cao BPeng MQing YLiu JKang GLi BFletcher K(2022) Web API recommendation via combining graph attention representation and deep factorization machines quality prediction Concurrency and Computation: Practice and Experience10.1002/cpe.706934:21Online publication date: 10-Jun-2022
https://doi.org/10.1002/cpe.7069
Badsha SYi XKhalil ILiu DNepal SBertino ELam K(2021)Privacy Preserving Location-Aware Personalized Web Service RecommendationsIEEE Transactions on Services Computing10.1109/TSC.2018.283958714:3(791-804)Online publication date: 1-May-2021
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Li XWang LZhu CLiu Z(2021)Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop-floorJournal of Manufacturing Systems10.1016/j.jmsy.2020.08.00358(281-292)Online publication date: Jan-2021
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