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Melnikov, E. Hüllermeier, D. Kaimann, B. Frick, and Pritha Gupta, “Pairwise versus Pointwise Ranking: A Case Study,” Schedae Informaticae, vol. 25, 2016.","short":"V. Melnikov, E. Hüllermeier, D. Kaimann, B. Frick, Pritha Gupta, Schedae Informaticae 25 (2016).","mla":"Melnikov, Vitalik, et al. “Pairwise versus Pointwise Ranking: A Case Study.” Schedae Informaticae, vol. 25, Uniwersytet Jagiellonski - Wydawnictwo Uniwersytetu Jagiellonskiego, 2016, doi:10.4467/20838476si.16.006.6187.","bibtex":"@article{Melnikov_Hüllermeier_Kaimann_Frick_Gupta_2016, title={Pairwise versus Pointwise Ranking: A Case Study}, volume={25}, DOI={10.4467/20838476si.16.006.6187}, journal={Schedae Informaticae}, publisher={Uniwersytet Jagiellonski - Wydawnictwo Uniwersytetu Jagiellonskiego}, author={Melnikov, Vitalik and Hüllermeier, Eyke and Kaimann, Daniel and Frick, Bernd and Gupta, Pritha }, year={2016} }","apa":"Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., & Gupta, Pritha . (2016). 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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. 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Hüllermeier, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.","ieee":"V. Melnikov and E. Hüllermeier, “Learning to Aggregate Using Uninorms,” in Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 756–71. LNCS, 2016. https://doi.org/10.1007/978-3-319-46227-1_47.","apa":"Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47","ama":"Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016). 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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. Experimental results for a corresponding model are presented for a review data set, for which the aggregation problem consists of combining different reviewer opinions about a paper into an overall decision of acceptance or rejection.","lang":"eng"}],"ddc":["040"],"user_id":"15504","author":[{"full_name":"Melnikov, Vitaly","first_name":"Vitaly","id":"58747","last_name":"Melnikov"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"file_date_updated":"2018-03-21T12:32:44Z","publication":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)","file":[{"date_created":"2018-03-21T12:32:44Z","file_name":"184-chp_3A10.1007_2F978-3-319-46227-1_47.pdf","access_level":"closed","file_id":"1533","creator":"florida","file_size":472159,"success":1,"relation":"main_file","date_updated":"2018-03-21T12:32:44Z","content_type":"application/pdf"}],"has_accepted_license":"1","status":"public","date_created":"2017-10-17T12:41:27Z","date_updated":"2022-01-06T06:53:32Z","doi":"10.1007/978-3-319-46227-1_47","series_title":"LNCS","language":[{"iso":"eng"}],"title":"Learning to Aggregate Using Uninorms","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"11","name":"SFB 901 - Subprojekt B3"},{"_id":"3","name":"SFB 901 - Project Area B"}]},{"_id":"10785","date_updated":"2022-01-06T06:50:50Z","citation":{"mla":"Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, Springer, 2016.","bibtex":"@inbook{Fürnkranz_Hüllermeier_2016, title={Preference Learning}, booktitle={Encyclopedia of Machine Learning and Data Mining}, publisher={Springer}, author={Fürnkranz, J. and Hüllermeier, Eyke}, editor={Sammut, C. and Webb, G.I.Editors}, year={2016} }","chicago":"Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb. Springer, 2016.","apa":"Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.","ama":"Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.","ieee":"J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G. I. Webb, Eds. Springer, 2016.","short":"J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016."},"type":"encyclopedia_article","year":"2016","language":[{"iso":"eng"}],"title":"Preference Learning","user_id":"49109","publication":"Encyclopedia of Machine Learning and Data Mining","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"author":[{"last_name":"Fürnkranz","first_name":"J.","full_name":"Fürnkranz, J."},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publisher":"Springer","editor":[{"full_name":"Sammut, C.","first_name":"C.","last_name":"Sammut"},{"full_name":"Webb, G.I.","first_name":"G.I.","last_name":"Webb"}],"date_created":"2019-07-10T16:00:23Z","status":"public"}]