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A probabilistic latent factor approach to service ranking

Cassar, G, Barnaghi, P and Moessner, K (2011) A probabilistic latent factor approach to service ranking

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Abstract

In this paper we investigate the use of probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors provide a model to represent service descriptions of any type in vector form. With this conversion, heterogeneous service descriptions can be represented on the same homogeneous plane thus achieving interoperability between different service description technologies. Automated service discovery and ranking is achieved by extracting latent factors from queries and representing the queries in vector form. Vector algebra can then be used to match services to the query. This approach is scalable to large service repositories and provides an efficient mechanism for publishing new services after the system is deployed. © 2011 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Cassar, GUNSPECIFIEDUNSPECIFIED
Barnaghi, PUNSPECIFIEDUNSPECIFIED
Moessner, KUNSPECIFIEDUNSPECIFIED
Date : 2011
Identification Number : https://doi.org/10.1109/ICCP.2011.6047850
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 15:02
Last Modified : 28 Mar 2017 15:02
URI: http://epubs.surrey.ac.uk/id/eprint/127283

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