SMS: A framework for service discovery by incorporating social media information

Tingting LIANG, Liang CHEN, Jian WU, Guandong XU, Zhaohui WU

Research output: Contribution to journalArticlespeer-review

9 Citations (Scopus)

Abstract

With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches. Copyright © 2016 IEEE.

Original languageEnglish
Pages (from-to)384-397
JournalIEEE Transactions on Services Computing
Volume12
Issue number3
Early online dateNov 2016
DOIs
Publication statusPublished - May 2019

Citation

Liang, T., Chen, L., Wu, J., Xu, G., & Wu, Z. (2016). SMS: A framework for service discovery by incorporating social media information. IEEE Transactions on Services Computing, 12(3), 384-397. https://doi.org/10.1109/TSC.2016.2631521

Keywords

  • Service discovery
  • Social media
  • Weight learning
  • Social factors combination

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