[{"language":[{"iso":"eng"}],"_id":"65182","department":[{"_id":"179"}],"user_id":"26032","abstract":[{"lang":"eng","text":"<jats:p>The aggregation of rating metrics in reputation systems is crucial for mitigating information overload by condensing customer rating distributions into singular valence scores. While platforms typically employ technical aggregation functions, such as the arithmetic mean to capture product quality, it remains unclear whether these functions align with customers' innate aggregation patterns. To address this knowledge gap, we designed a controlled economic decision experiment to elicit customers' aggregation principles by analyzing their product ranking decisions and contrasting these with various reference functions. Our findings indicate that, on average, customers aggregate rating information in accordance with the arithmetic mean. However, a granular analysis at the individual level reveals significant heterogeneity in aggregation behavior, with a substantial cluster exhibiting binary patterns that focus equally on negative (1-2 star) and positive (4-5 star) ratings. Additional clusters concentrate on negative feedback, particularly 1-star ratings or 1-2 star ratings collectively. Notably, these inherent aggregation patterns exhibit stability across variations in numerical information presentation and are not significantly influenced by individual characteristics, such as online shopping experience, risk attitudes, or demographics. These findings suggest that while the arithmetic mean captures average consumer behavior, platforms could benefit from offering customizable aggregation options to better cater to diverse user preferences for processing rating distributions. By doing so, platforms can enhance the effectiveness of their reputation systems and improve the overall quality of decision-making for consumers.</jats:p>"}],"status":"public","publication":"SSRN Electronic Journal","type":"journal_article","title":"Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment","doi":"http://dx.doi.org/10.2139/ssrn.6201258","date_updated":"2026-03-27T21:55:03Z","publisher":"Elsevier BV","author":[{"first_name":"Dirk","last_name":"van Straaten","full_name":"van Straaten, Dirk","id":"10311"},{"full_name":"Mir Djawadi, Behnud","id":"26032","last_name":"Mir Djawadi","orcid":"0000-0002-6271-5912","first_name":"Behnud"},{"last_name":"Melnikov","id":"58747","full_name":"Melnikov, Vitalik","first_name":"Vitalik"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"first_name":"René","id":"111","full_name":"Fahr, René","last_name":"Fahr"}],"date_created":"2026-03-27T16:21:55Z","year":"2026","citation":{"bibtex":"@article{van Straaten_Mir Djawadi_Melnikov_Hüllermeier_Fahr_2026, title={Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment}, DOI={<a href=\"http://dx.doi.org/10.2139/ssrn.6201258\">http://dx.doi.org/10.2139/ssrn.6201258</a>}, journal={SSRN Electronic Journal}, publisher={Elsevier BV}, author={van Straaten, Dirk and Mir Djawadi, Behnud and Melnikov, Vitalik and Hüllermeier, Eyke and Fahr, René}, year={2026} }","short":"D. van Straaten, B. Mir Djawadi, V. Melnikov, E. Hüllermeier, R. Fahr, SSRN Electronic Journal (2026).","mla":"van Straaten, Dirk, et al. “Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment.” <i>SSRN Electronic Journal</i>, Elsevier BV, 2026, doi:<a href=\"http://dx.doi.org/10.2139/ssrn.6201258\">http://dx.doi.org/10.2139/ssrn.6201258</a>.","apa":"van Straaten, D., Mir Djawadi, B., Melnikov, V., Hüllermeier, E., &#38; Fahr, R. (2026). Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment. <i>SSRN Electronic Journal</i>. <a href=\"http://dx.doi.org/10.2139/ssrn.6201258\">http://dx.doi.org/10.2139/ssrn.6201258</a>","ama":"van Straaten D, Mir Djawadi B, Melnikov V, Hüllermeier E, Fahr R. Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment. <i>SSRN Electronic Journal</i>. Published online 2026. doi:<a href=\"http://dx.doi.org/10.2139/ssrn.6201258\">http://dx.doi.org/10.2139/ssrn.6201258</a>","chicago":"Straaten, Dirk van, Behnud Mir Djawadi, Vitalik Melnikov, Eyke Hüllermeier, and René Fahr. “Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment.” <i>SSRN Electronic Journal</i>, 2026. <a href=\"http://dx.doi.org/10.2139/ssrn.6201258\">http://dx.doi.org/10.2139/ssrn.6201258</a>.","ieee":"D. van Straaten, B. Mir Djawadi, V. Melnikov, E. Hüllermeier, and R. Fahr, “Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment,” <i>SSRN Electronic Journal</i>, 2026, doi: <a href=\"http://dx.doi.org/10.2139/ssrn.6201258\">http://dx.doi.org/10.2139/ssrn.6201258</a>."},"publication_status":"published"},{"type":"conference","publication":"Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)","status":"public","abstract":[{"lang":"eng","text":"Testing is one of the most frequent means of quality assurance for software. Property-based testing aims at generating test suites for checking code against user-defined properties. Test input generation is, however, most often independent of the property to be checked, and is instead based on random or user-defined data generation.In this paper, we present property-driven unit testing of functions with numerical inputs and outputs. Alike property-based testing, it allows users to define the properties to be tested for. Contrary to property-based testing, it also uses the property for a targeted generation of test inputs. Our approach is a form of learning-based testing where we first of all learn a model of a given black-box function using standard machine learning algorithms, and in a second step use model and property for test input generation. This allows us to test both predefined functions as well as machine learned regression models. Our experimental evaluation shows that our property-driven approach is more effective than standard property-based testing techniques."}],"user_id":"477","department":[{"_id":"7"}],"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"11","name":"SFB 901 - B3: SFB 901 - Subproject B3"}],"_id":"32311","language":[{"iso":"eng"}],"citation":{"chicago":"Sharma, Arnab, Vitaly Melnikov, Eyke Hüllermeier, and Heike Wehrheim. “Property-Driven Testing of Black-Box Functions.” In <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, 113–23. IEEE, 2022.","ieee":"A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven Testing of Black-Box Functions,” in <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, 2022, pp. 113–123.","apa":"Sharma, A., Melnikov, V., Hüllermeier, E., &#38; Wehrheim, H. (2022). Property-Driven Testing of Black-Box Functions. <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, 113–123.","ama":"Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of Black-Box Functions. In: <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>. IEEE; 2022:113-123.","bibtex":"@inproceedings{Sharma_Melnikov_Hüllermeier_Wehrheim_2022, title={Property-Driven Testing of Black-Box Functions}, booktitle={Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)}, publisher={IEEE}, author={Sharma, Arnab and Melnikov, Vitaly and Hüllermeier, Eyke and Wehrheim, Heike}, year={2022}, pages={113–123} }","mla":"Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” <i>Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)</i>, IEEE, 2022, pp. 113–23.","short":"A. Sharma, V. Melnikov, E. Hüllermeier, H. Wehrheim, in: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–123."},"page":"113-123","year":"2022","author":[{"last_name":"Sharma","full_name":"Sharma, Arnab","id":"67200","first_name":"Arnab"},{"full_name":"Melnikov, Vitaly","id":"58747","last_name":"Melnikov","first_name":"Vitaly"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"},{"first_name":"Heike","last_name":"Wehrheim","full_name":"Wehrheim, Heike","id":"573"}],"date_created":"2022-07-01T11:18:03Z","publisher":"IEEE","date_updated":"2022-07-01T11:21:36Z","title":"Property-Driven Testing of Black-Box Functions"},{"intvolume":"        72","citation":{"apa":"van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., &#38; Fahr, R. (2021). <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i> (Vol. 72).","bibtex":"@book{van Straaten_Melnikov_Hüllermeier_Mir Djawadi_Fahr_2021, series={Working Papers Dissertations}, title={Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes}, volume={72}, author={van Straaten, Dirk and Melnikov, Vitalik and Hüllermeier, Eyke and Mir Djawadi, Behnud and Fahr, René}, year={2021}, collection={Working Papers Dissertations} }","short":"D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, R. Fahr, Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes, 2021.","mla":"van Straaten, Dirk, et al. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. 2021.","ama":"van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. Vol 72.; 2021.","chicago":"Straaten, Dirk van, Vitalik Melnikov, Eyke Hüllermeier, Behnud Mir Djawadi, and René Fahr. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. Vol. 72. Working Papers Dissertations, 2021.","ieee":"D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, and R. Fahr, <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>, vol. 72. 2021."},"year":"2021","title":"Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes","volume":72,"author":[{"first_name":"Dirk","last_name":"van Straaten","full_name":"van Straaten, Dirk","id":"10311"},{"first_name":"Vitalik","full_name":"Melnikov, Vitalik","id":"58747","last_name":"Melnikov"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"full_name":"Mir Djawadi, Behnud","id":"26032","orcid":"0000-0002-6271-5912","last_name":"Mir Djawadi","first_name":"Behnud"},{"full_name":"Fahr, René","id":"111","last_name":"Fahr","first_name":"René"}],"date_created":"2023-06-15T08:23:33Z","date_updated":"2023-07-05T07:27:17Z","status":"public","abstract":[{"lang":"eng","text":"Aggregation metrics in reputation systems are important for overcoming information overload. When using these metrics, technical aggregation functions such as the arithmetic mean are implemented to measure the valence of product ratings. However, it is unclear whether the implemented aggregation functions match the inherent aggregation patterns of customers. In our experiment, we elicit customers' aggregation heuristics and contrast these with reference functions. Our findings indicate that, overall, the arithmetic mean performs best in comparison with other aggregation functions. However, our analysis on an individual level reveals heterogeneous aggregation patterns. Major clusters exhibit a binary bias (i.e., an over-weighting of moderate ratings and under-weighting of extreme ratings) in combination with the arithmetic mean. Minor clusters focus on 1-star ratings or negative (i.e., 1-star and 2-star) ratings. Thereby, inherent aggregation patterns are neither affected by variation of provided information nor by individual characteristics such as experience, risk attitudes, or demographics."}],"type":"working_paper","language":[{"iso":"eng"}],"user_id":"477","series_title":"Working Papers Dissertations","_id":"45616","project":[{"_id":"8","name":"SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen (Subproject A4)","grant_number":"160364472"},{"name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","_id":"1","grant_number":"160364472"},{"_id":"2","name":"SFB 901 - A: SFB 901 - Project Area A"}]},{"type":"conference","status":"public","editor":[{"first_name":"Sinno","full_name":"Jialin Pan, Sinno","last_name":"Jialin Pan"},{"first_name":"Masashi","full_name":"Sugiyama, Masashi","last_name":"Sugiyama"}],"department":[{"_id":"355"}],"user_id":"48192","series_title":"Proceedings of Machine Learning Research","_id":"19953","file_date_updated":"2020-10-08T11:24:29Z","has_accepted_license":"1","publication_status":"published","intvolume":"       129","page":"49-64","citation":{"ama":"Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>. Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.","chicago":"Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.","ieee":"C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, Bangkok, Thailand, 2020, vol. 129, pp. 49–64.","short":"C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR, Bangkok, Thailand, 2020, pp. 49–64.","mla":"Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.","bibtex":"@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand}, series={Proceedings of Machine Learning Research}, title={A Novel Higher-order Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR}, author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings of Machine Learning Research} }","apa":"Damke, C., Melnikov, V., &#38; Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan &#38; M. Sugiyama (Eds.), <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i> (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR."},"place":"Bangkok, Thailand","volume":129,"author":[{"first_name":"Clemens","full_name":"Damke, Clemens","id":"48192","orcid":"0000-0002-0455-0048","last_name":"Damke"},{"last_name":"Melnikov","full_name":"Melnikov, Vitaly","id":"58747","first_name":"Vitaly"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"date_updated":"2022-01-06T06:54:17Z","oa":"1","conference":{"location":"Bangkok, Thailand","end_date":"2020-11-20","start_date":"2020-11-18","name":"Asian Conference on Machine Learning"},"publication":"Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)","file":[{"creator":"cdamke","date_created":"2020-10-08T10:54:48Z","date_updated":"2020-10-08T11:21:00Z","file_id":"19954","file_name":"damke20.pdf","access_level":"open_access","file_size":771137,"content_type":"application/pdf","relation":"main_file"},{"content_type":"application/pdf","relation":"supplementary_material","date_updated":"2020-10-08T11:24:29Z","date_created":"2020-10-08T10:54:59Z","creator":"cdamke","file_size":613163,"access_level":"open_access","file_id":"19955","file_name":"damke20-supp.pdf"}],"abstract":[{"lang":"eng","text":"Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs."}],"external_id":{"arxiv":["2007.00346"]},"language":[{"iso":"eng"}],"keyword":["graph neural networks","Weisfeiler-Lehman test","cycle detection"],"ddc":["006"],"quality_controlled":"1","year":"2020","date_created":"2020-10-08T10:48:38Z","publisher":"PMLR","title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution"},{"doi":"10.1016/j.jmva.2019.02.017","title":"Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA","date_created":"2019-11-15T10:43:26Z","author":[{"last_name":"Melnikov","id":"58747","full_name":"Melnikov, Vitaly","first_name":"Vitaly"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"oa":"1","date_updated":"2022-01-06T06:52:14Z","citation":{"chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” In <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>, 2019. <a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">https://doi.org/10.1016/j.jmva.2019.02.017</a>.","ieee":"V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA,” in <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>, 2019.","ama":"Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In: <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>. ; 2019. doi:<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>","short":"V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.","bibtex":"@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>}, booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2019} }","mla":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>, 2019, doi:<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>.","apa":"Melnikov, V., &#38; Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>. <a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">https://doi.org/10.1016/j.jmva.2019.02.017</a>"},"year":"2019","has_accepted_license":"1","publication_status":"published","language":[{"iso":"eng"}],"file_date_updated":"2020-02-28T12:47:07Z","ddc":["000"],"department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"user_id":"477","_id":"15007","project":[{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901","_id":"1"}],"status":"public","file":[{"access_level":"open_access","file_id":"16156","file_name":"learning-to-aggregate-owa.pdf","file_size":2331320,"creator":"lettmann","date_created":"2020-02-28T12:47:07Z","date_updated":"2020-02-28T12:47:07Z","relation":"main_file","content_type":"application/pdf"}],"publication":"Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)","type":"conference"},{"project":[{"_id":"1","name":"SFB 901"},{"_id":"11","name":"SFB 901 - Subprojekt B3"},{"_id":"3","name":"SFB 901 - Project Area B"}],"_id":"184","series_title":"LNCS","user_id":"15504","department":[{"_id":"355"}],"file_date_updated":"2018-03-21T12:32:44Z","type":"conference","status":"public","date_updated":"2022-01-06T06:53:32Z","author":[{"id":"58747","full_name":"Melnikov, Vitaly","last_name":"Melnikov","first_name":"Vitaly"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"doi":"10.1007/978-3-319-46227-1_47","has_accepted_license":"1","citation":{"apa":"Melnikov, V., &#38; Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i> (pp. 756–771). <a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">https://doi.org/10.1007/978-3-319-46227-1_47</a>","bibtex":"@inproceedings{Melnikov_Hüllermeier_2016, series={LNCS}, title={Learning to Aggregate Using Uninorms}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">10.1007/978-3-319-46227-1_47</a>}, booktitle={Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2016}, pages={756–771}, collection={LNCS} }","mla":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>, 2016, pp. 756–71, doi:<a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">10.1007/978-3-319-46227-1_47</a>.","short":"V. Melnikov, E. 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 <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>, 2016, pp. 756–771.","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” In <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>, 756–71. LNCS, 2016. <a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">https://doi.org/10.1007/978-3-319-46227-1_47</a>.","ama":"Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>. LNCS. ; 2016:756-771. doi:<a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">10.1007/978-3-319-46227-1_47</a>"},"page":"756-771","ddc":["040"],"language":[{"iso":"eng"}],"publication":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)","abstract":[{"text":"In this paper, we propose a framework for a class of learning problems that we refer to as “learning to aggregate”. 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"}],"file":[{"date_created":"2018-03-21T12:32:44Z","creator":"florida","date_updated":"2018-03-21T12:32:44Z","access_level":"closed","file_id":"1533","file_name":"184-chp_3A10.1007_2F978-3-319-46227-1_47.pdf","file_size":472159,"content_type":"application/pdf","relation":"main_file","success":1}],"date_created":"2017-10-17T12:41:27Z","title":"Learning to Aggregate Using Uninorms","year":"2016"},{"type":"conference","publication":"European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy","status":"public","_id":"10223","user_id":"49109","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"language":[{"iso":"eng"}],"year":"2016","citation":{"ieee":"V. Melnikov and E. Hüllermeier, “Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016,” in <i>European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy</i>, 2016, pp. 756–771.","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms,  in Proceedings ECML/PKDD-2016.” In <i>European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy</i>, 756–71, 2016.","ama":"Melnikov V, Hüllermeier E. Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In: <i>European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy</i>. ; 2016:756-771.","short":"V. Melnikov, E. Hüllermeier, in: European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 756–771.","mla":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms,  in Proceedings ECML/PKDD-2016.” <i>European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy</i>, 2016, pp. 756–71.","bibtex":"@inproceedings{Melnikov_Hüllermeier_2016, title={Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016}, booktitle={European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2016}, pages={756–771} }","apa":"Melnikov, V., &#38; Hüllermeier, E. (2016). Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In <i>European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy</i> (pp. 756–771)."},"page":"756-771","date_updated":"2022-01-06T06:50:32Z","author":[{"first_name":"Vitaly","last_name":"Melnikov","full_name":"Melnikov, Vitaly","id":"58747"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"date_created":"2019-06-11T14:51:30Z","title":"Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016"}]