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DOI: 10.14569/IJACSA.2013.040637
PDF

Correlated Topic Model for Web Services Ranking

Author 1: Mustapha AZNAG
Author 2: Mohamed QUAFAFOU
Author 3: Zahi JARIR

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 6, 2013.

  • Abstract and Keywords
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Abstract: With the increasing number of published Web services providing similar functionalities, it’s very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn).

Keywords: Web service, Data Representation, Discovery, Ranking, Machine Learning, Topic Models

Mustapha AZNAG, Mohamed QUAFAFOU and Zahi JARIR, “Correlated Topic Model for Web Services Ranking” International Journal of Advanced Computer Science and Applications(IJACSA), 4(6), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040637

@article{AZNAG2013,
title = {Correlated Topic Model for Web Services Ranking},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040637},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040637},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {6},
author = {Mustapha AZNAG and Mohamed QUAFAFOU and Zahi JARIR}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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