Abstract
A Web service is a method of communication between two electronic devices over a network. Web services have been widely employed for building service-oriented applications. Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high- quality recommendation on sparse data. In this paper, a novel collaborative filtering-based Web service recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. WEB services are software components designed to support interoperable machine-to-machine interaction over a network, usually the Internet. Web service employs WSDL (Web Service Description Language) for interface description and SOAP (Simple Object Access Protocol) for exchanging structured information. Web services have been widely employed by both enterprises and individual developers for building service oriented applications. When developing service-oriented applications, developers first design the business process according to requirements, and then try to find and reuse existing services to build the process. Collaborative filtering is valuable in e-commerce, and for direct recommendations for music, movies, news etc. Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables.
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