---
_id: '65182'
abstract:
- lang: eng
text: 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.
author:
- first_name: Dirk
full_name: van Straaten, Dirk
id: '10311'
last_name: van Straaten
- first_name: Behnud
full_name: Mir Djawadi, Behnud
id: '26032'
last_name: Mir Djawadi
orcid: 0000-0002-6271-5912
- first_name: Vitalik
full_name: Melnikov, Vitalik
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: René
full_name: Fahr, René
id: '111'
last_name: Fahr
citation:
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.
SSRN Electronic Journal. Published online 2026. doi:http://dx.doi.org/10.2139/ssrn.6201258
apa: van Straaten, D., Mir Djawadi, B., Melnikov, V., Hüllermeier, E., & Fahr,
R. (2026). Aggregation Processes in Customer Rating Systems - Insights from an
Economic Decision Experiment. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.6201258
bibtex: '@article{van Straaten_Mir Djawadi_Melnikov_Hüllermeier_Fahr_2026, title={Aggregation
Processes in Customer Rating Systems - Insights from an Economic Decision Experiment},
DOI={http://dx.doi.org/10.2139/ssrn.6201258},
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} }'
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.” SSRN Electronic Journal, 2026. http://dx.doi.org/10.2139/ssrn.6201258.
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,” SSRN Electronic Journal, 2026, doi: http://dx.doi.org/10.2139/ssrn.6201258.'
mla: van Straaten, Dirk, et al. “Aggregation Processes in Customer Rating Systems
- Insights from an Economic Decision Experiment.” SSRN Electronic Journal,
Elsevier BV, 2026, doi:http://dx.doi.org/10.2139/ssrn.6201258.
short: D. van Straaten, B. Mir Djawadi, V. Melnikov, E. Hüllermeier, R. Fahr, SSRN
Electronic Journal (2026).
date_created: 2026-03-27T16:21:55Z
date_updated: 2026-03-27T21:55:03Z
department:
- _id: '179'
doi: http://dx.doi.org/10.2139/ssrn.6201258
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
publisher: Elsevier BV
status: public
title: Aggregation Processes in Customer Rating Systems - Insights from an Economic
Decision Experiment
type: journal_article
user_id: '26032'
year: '2026'
...
---
_id: '32311'
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.
author:
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of
Black-Box Functions. In: Proceedings of the 10th IEEE/ACM International Conference
on Formal Methods in Software Engineering (FormaliSE). IEEE; 2022:113-123.'
apa: Sharma, A., Melnikov, V., Hüllermeier, E., & Wehrheim, H. (2022). Property-Driven
Testing of Black-Box Functions. Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE), 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} }'
chicago: Sharma, Arnab, Vitaly Melnikov, Eyke Hüllermeier, and Heike Wehrheim. “Property-Driven
Testing of Black-Box Functions.” In Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE), 113–23.
IEEE, 2022.
ieee: A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven
Testing of Black-Box Functions,” in Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE), 2022, pp.
113–123.
mla: Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” Proceedings
of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering
(FormaliSE), 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.'
date_created: 2022-07-01T11:18:03Z
date_updated: 2022-07-01T11:21:36Z
department:
- _id: '7'
language:
- iso: eng
page: 113-123
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'
publication: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods
in Software Engineering (FormaliSE)
publisher: IEEE
status: public
title: Property-Driven Testing of Black-Box Functions
type: conference
user_id: '477'
year: '2022'
...
---
_id: '45616'
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.
author:
- first_name: Dirk
full_name: van Straaten, Dirk
id: '10311'
last_name: van Straaten
- first_name: Vitalik
full_name: Melnikov, Vitalik
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Behnud
full_name: Mir Djawadi, Behnud
id: '26032'
last_name: Mir Djawadi
orcid: 0000-0002-6271-5912
- first_name: René
full_name: Fahr, René
id: '111'
last_name: Fahr
citation:
ama: 'van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. Accounting
for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation
Processes. Vol 72.; 2021.'
apa: 'van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., & Fahr,
R. (2021). Accounting for Heuristics in Reputation Systems: An Interdisciplinary
Approach on Aggregation Processes (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} }'
chicago: 'Straaten, Dirk van, Vitalik Melnikov, Eyke Hüllermeier, Behnud Mir Djawadi,
and René Fahr. Accounting for Heuristics in Reputation Systems: An Interdisciplinary
Approach on Aggregation Processes. Vol. 72. Working Papers Dissertations,
2021.'
ieee: 'D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, and R. Fahr,
Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach
on Aggregation Processes, vol. 72. 2021.'
mla: 'van Straaten, Dirk, et al. Accounting for Heuristics in Reputation Systems:
An Interdisciplinary Approach on Aggregation Processes. 2021.'
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.'
date_created: 2023-06-15T08:23:33Z
date_updated: 2023-07-05T07:27:17Z
intvolume: ' 72'
language:
- iso: eng
project:
- _id: '8'
grant_number: '160364472'
name: 'SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen
(Subproject A4)'
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '2'
name: 'SFB 901 - A: SFB 901 - Project Area A'
series_title: Working Papers Dissertations
status: public
title: 'Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach
on Aggregation Processes'
type: working_paper
user_id: '477'
volume: 72
year: '2021'
...
---
_id: '19953'
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.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman
Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th
Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of
Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.'
apa: 'Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order
Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
(Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.'
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} }'
chicago: 'Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order
Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference
on Machine Learning (ACML 2020), 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 Proceedings of the 12th Asian Conference on Machine
Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.
mla: Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.”
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, 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.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-10-08T10:48:38Z
date_updated: 2022-01-06T06:54:17Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Sinno
full_name: Jialin Pan, Sinno
last_name: Jialin Pan
- first_name: Masashi
full_name: Sugiyama, Masashi
last_name: Sugiyama
external_id:
arxiv:
- '2007.00346'
file:
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:48Z
date_updated: 2020-10-08T11:21:00Z
file_id: '19954'
file_name: damke20.pdf
file_size: 771137
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:59Z
date_updated: 2020-10-08T11:24:29Z
file_id: '19955'
file_name: damke20-supp.pdf
file_size: 613163
relation: supplementary_material
file_date_updated: 2020-10-08T11:24:29Z
has_accepted_license: '1'
intvolume: ' 129'
keyword:
- graph neural networks
- Weisfeiler-Lehman test
- cycle detection
language:
- iso: eng
oa: '1'
page: 49-64
place: Bangkok, Thailand
publication: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
publication_status: published
publisher: PMLR
quality_controlled: '1'
series_title: Proceedings of Machine Learning Research
status: public
title: A Novel Higher-order Weisfeiler-Lehman Graph Convolution
type: conference
user_id: '48192'
volume: 129
year: '2020'
...
---
_id: '15007'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017'
apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101).
https://doi.org/10.1016/j.jmva.2019.02.017'
bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate:
Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017},
booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings
of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier,
Eyke}, year={2019} }'
chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101),
2019. https://doi.org/10.1016/j.jmva.2019.02.017.'
ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101), 2019.'
mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the
Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference
on Machine Learning (Proceedings of Machine Learning Research, 101), 2019,
doi:10.1016/j.jmva.2019.02.017.'
short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine
Learning (Proceedings of Machine Learning Research, 101), 2019.'
date_created: 2019-11-15T10:43:26Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
- _id: '7'
doi: 10.1016/j.jmva.2019.02.017
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:47:07Z
date_updated: 2020-02-28T12:47:07Z
file_id: '16156'
file_name: learning-to-aggregate-owa.pdf
file_size: 2331320
relation: main_file
file_date_updated: 2020-02-28T12:47:07Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of
Machine Learning Research, 101)
publication_status: published
status: public
title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for
OWA'
type: conference
user_id: '477'
year: '2019'
...
---
_id: '184'
abstract:
- lang: eng
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.
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
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). LNCS. ; 2016:756-771. doi: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
bibtex: '@inproceedings{Melnikov_Hüllermeier_2016, series={LNCS}, title={Learning
to Aggregate Using Uninorms}, DOI={10.1007/978-3-319-46227-1_47},
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} }'
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.
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.
mla: Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.”
Proceedings of the Joint European Conference on Machine Learning and Knowledge
Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–71, doi:10.1007/978-3-319-46227-1_47.
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.'
date_created: 2017-10-17T12:41:27Z
date_updated: 2022-01-06T06:53:32Z
ddc:
- '040'
department:
- _id: '355'
doi: 10.1007/978-3-319-46227-1_47
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T12:32:44Z
date_updated: 2018-03-21T12:32:44Z
file_id: '1533'
file_name: 184-chp_3A10.1007_2F978-3-319-46227-1_47.pdf
file_size: 472159
relation: main_file
success: 1
file_date_updated: 2018-03-21T12:32:44Z
has_accepted_license: '1'
language:
- iso: eng
page: 756-771
project:
- _id: '1'
name: SFB 901
- _id: '11'
name: SFB 901 - Subprojekt B3
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the Joint European Conference on Machine Learning and
Knowledge Discovery in Databases (ECML/PKDD 2016)
series_title: LNCS
status: public
title: Learning to Aggregate Using Uninorms
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '10223'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Learning to aggregate using uninorms, in Proceedings
ECML/PKDD-2016. In: European Conference on Machine Learning and Knowledge Discovery
in Databases, Part II, Riva Del Garda, Italy. ; 2016:756-771.'
apa: Melnikov, V., & Hüllermeier, E. (2016). Learning to aggregate using uninorms,
in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and
Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
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}
}'
chicago: Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms,
in Proceedings ECML/PKDD-2016.” In European Conference on Machine Learning
and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 756–71,
2016.
ieee: V. Melnikov and E. Hüllermeier, “Learning to aggregate using uninorms, in
Proceedings ECML/PKDD-2016,” 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.” European Conference on Machine Learning and
Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp.
756–71.
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.'
date_created: 2019-06-11T14:51:30Z
date_updated: 2022-01-06T06:50:32Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 756-771
publication: European Conference on Machine Learning and Knowledge Discovery in Databases,
Part II, Riva del Garda, Italy
status: public
title: Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016
type: conference
user_id: '49109'
year: '2016'
...