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In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach."}],"has_accepted_license":"1","status":"public","date_created":"2017-10-17T12:41:29Z","publisher":"IEEE","author":[{"last_name":"Platenius","first_name":"Marie Christin","full_name":"Platenius, Marie Christin"},{"full_name":"Shaker, Ammar","first_name":"Ammar","last_name":"Shaker"},{"last_name":"Becker","first_name":"Matthias","full_name":"Becker, Matthias"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"last_name":"Schäfer","first_name":"Wilhelm","full_name":"Schäfer, Wilhelm"}],"publication":"IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017","file_date_updated":"2018-03-21T12:30:31Z","file":[{"file_id":"1529","creator":"florida","file_size":5225413,"success":1,"relation":"main_file","date_updated":"2018-03-21T12:30:31Z","content_type":"application/pdf","date_created":"2018-03-21T12:30:31Z","file_name":"190-07755807.pdf","access_level":"closed"}],"issue":"8","_id":"190","year":"2016","type":"journal_article","citation":{"apa":"Platenius, M. 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Roughly, learning-to-aggregate problems are supervised machine learning problems, in which instances are represented in the form of a composition of a (variable) number on constituents; such compositions are associated with an evaluation, score, or label, which is the target of the prediction task, and which can presumably be modeled in the form of a suitable aggregation of the properties of its constituents. Our learning-to-aggregate framework establishes a close connection between machine learning and a branch of mathematics devoted to the systematic study of aggregation functions. We specifically focus on a class of functions called uninorms, which combine conjunctive and disjunctive modes of aggregation. 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