Improving the performance of web service recommenders using semantic similarity

This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorith...

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Detalles Bibliográficos
Autores principales:	Adán Coello, Juan Manuel, Tobar, Carlos Miguel, Yuming, Yang
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2014
Materias:	Ciencias Informáticas Semantics Web-based services Information filtering
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/41804 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct14-3.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-41804
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Semantics Web-based services Information filtering
spellingShingle	Ciencias Informáticas Semantics Web-based services Information filtering Adán Coello, Juan Manuel Tobar, Carlos Miguel Yuming, Yang Improving the performance of web service recommenders using semantic similarity
topic_facet	Ciencias Informáticas Semantics Web-based services Information filtering
description	This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorithm, using the k-NN method, and a model-based algorithm, using the k-means method. In both algorithms, similarity between users is computed using the Pearson Correlation Coefficient (PCC). One of the limitations of using the PCC in this context is that in those instances where users have not rated items in common it is not possible to compute their similarity. In addition, when the number of common items that were rated is low, the reliability of the computed similarity degree may also be low. To overcome these limitations, the presented algorithms compute the similarity between two users taking into account services that both users accessed and also semantically similar services. Likewise, to predict the rating for a not yet accessed target service, the algorithms consider the ratings that neighbor users assigned to the target service, as is normally the case, while also considering the ratings assigned to services that are semantically similar to the target service. The experiments described in the paper show that this approach has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.
format	Articulo Articulo
author	Adán Coello, Juan Manuel Tobar, Carlos Miguel Yuming, Yang
author_facet	Adán Coello, Juan Manuel Tobar, Carlos Miguel Yuming, Yang
author_sort	Adán Coello, Juan Manuel
title	Improving the performance of web service recommenders using semantic similarity
title_short	Improving the performance of web service recommenders using semantic similarity
title_full	Improving the performance of web service recommenders using semantic similarity
title_fullStr	Improving the performance of web service recommenders using semantic similarity
title_full_unstemmed	Improving the performance of web service recommenders using semantic similarity
title_sort	improving the performance of web service recommenders using semantic similarity
publishDate	2014
url	http://sedici.unlp.edu.ar/handle/10915/41804 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct14-3.pdf
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bdutipo_str	Repositorios
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Improving the performance of web service recommenders using semantic similarity

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