The Competition between different Web Service Providers to enhance their services and to increase the users' usage of their provided services raises the idea of our research. Our research is focusing on increasing the number of services that User or Developer will use. We proposed a web service’s recommendation model by applying the data mining techniques like Apriori algorithm to suggest another web service beside the one he got from the discovery process based on the user’s History. For implementing our model, we used a curated source for web services and users, which also contains a complete information about users and their web services usage. We found a BioCatalogue: our proposed
model was tested on a Curated Web Service Registry (BioCatalogue).and 70 % of users chose services from services that recommended by our model besides the discovered ones by BioCatalogue.
%0 Journal Article
%1 noauthororeditor
%A Prof.Dr.TorkeyI.Sultan,
%A Khedr, Dr. Ayman E.
%A Alsheref, Fahad Kamal
%D 2013
%J International Journal on Web Service Computing (IJWSC)
%K BioCatalogue Data Mining Recommendation System Web and discovery services
%N 4
%P 21-33
%R 10.5121/ijwsc.2013.4403
%T ADAPTIVE MODEL FOR WEB SERVICE RECOMMENDATION
%U http://airccse.org/journal/jwsc/papers/4413ijwsc03.pdf
%V 4
%X The Competition between different Web Service Providers to enhance their services and to increase the users' usage of their provided services raises the idea of our research. Our research is focusing on increasing the number of services that User or Developer will use. We proposed a web service’s recommendation model by applying the data mining techniques like Apriori algorithm to suggest another web service beside the one he got from the discovery process based on the user’s History. For implementing our model, we used a curated source for web services and users, which also contains a complete information about users and their web services usage. We found a BioCatalogue: our proposed
model was tested on a Curated Web Service Registry (BioCatalogue).and 70 % of users chose services from services that recommended by our model besides the discovered ones by BioCatalogue.
@article{noauthororeditor,
abstract = {The Competition between different Web Service Providers to enhance their services and to increase the users' usage of their provided services raises the idea of our research. Our research is focusing on increasing the number of services that User or Developer will use. We proposed a web service’s recommendation model by applying the data mining techniques like Apriori algorithm to suggest another web service beside the one he got from the discovery process based on the user’s History. For implementing our model, we used a curated source for web services and users, which also contains a complete information about users and their web services usage. We found a BioCatalogue: our proposed
model was tested on a Curated Web Service Registry (BioCatalogue).and 70 % of users chose services from services that recommended by our model besides the discovered ones by BioCatalogue.},
added-at = {2020-01-24T07:34:22.000+0100},
author = {Prof.Dr.TorkeyI.Sultan and Khedr, Dr. Ayman E. and Alsheref, Fahad Kamal},
biburl = {https://www.bibsonomy.org/bibtex/256affab885505e2ce497a2105c29d558/ijwsc},
doi = {10.5121/ijwsc.2013.4403},
interhash = {38d851c49b10c3c6d476fdb5fb13a9ba},
intrahash = {56affab885505e2ce497a2105c29d558},
issn = {0976 - 9811 (Online) ; 2230 - 7702 (print)},
journal = {International Journal on Web Service Computing (IJWSC)},
keywords = {BioCatalogue Data Mining Recommendation System Web and discovery services},
language = {English},
month = {December},
number = 4,
pages = {21-33},
timestamp = {2020-01-24T07:34:22.000+0100},
title = {ADAPTIVE MODEL FOR WEB SERVICE RECOMMENDATION},
url = {http://airccse.org/journal/jwsc/papers/4413ijwsc03.pdf},
volume = 4,
year = 2013
}