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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Framework for Transactional Service Selection Based on Crowdsourcing
verfasst von : Rafael Angarita, Maude Manouvrier, Marta Rukoz
Erschienen in: Mobile Web and Intelligent Information Systems
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The growing number of services in the Web providing the same functionality but different QoS (e.g., price, execution time, and availability) and transactional properties (e.g., compensable or not) has lead to the emergence of several approaches for service selection and recommendation. Some of these approaches use collaborative filtering, QoS prediction, service reputation, among others. Existing works lack from a way to integrate all those methods and benefit from their multiple perspectives to decide how to select a service. The problem tackled in this work is the selection of the most suitable service from a set of functionally equivalent services according to the opinions of multiple contributors. We propose a framework to easily rely on crowdsourcing for service selection, where crowdsourcing contributors can be independently developed services or human experts. Our framework emphasizes on the definition of a collaborative system to allow contributors to join and participate in the selection of services.