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 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.Facultad de Informátic

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 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.Facultad de Informátic

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 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.Facultad de Informátic

Web Services Discovery in a Pay-As-You-Go Fashion

Extensive effort has been brought forth to assist in web service discovery. In particular, classic Information Retrieval techniques are exploited to assess the similarity between two web services descriptions, while Semantic Web technologies are proposed to enhance semantic service descriptions. These approaches have greatly improved the quality and accuracy of service discovery. However, these works require hard up-front investment before offering powerful functionalities for service discovery, and they do not study how to discover web services in a pay-as-you-go fashion. In this paper, a framework based on dataspace techniques is proposed to discover web services in a pay-as-you-go fashion. In this framework, a loosely structured data model based on dataspace models is presented to describe web services and the relationships among them, and then keyword-based query is supported on top of this model by using the existing dataspace query language. To support similarity-based service discovery, dataspace techniques are extended to declare the similarity among web services, and a discovery algorithm is presented. In addition, a lightweight way adding semantics to the query processing is also shown in the paper. Finally, the differences between our work and previous works are discussed