CrowDM system
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Mining Complex Data Generated by Collaborative Platforms
Dmitry I. Ignatov, Alexandra Yu. Kaminskaya, Anastasia A. Bezzubt-
seva, Ekaterina L. Chernyak, Konstantin N. Blinkin, Daniil R. Ne- dumov, Olga N. Chugunova, Andrey V. Konstantinov, Nikita S. Ro-
mashkin, Fedor V. Strok, Daria A. Goncharova, Rostislav E. Yavorsky
BIR 2012 HSE, Nizhniy Novgorod
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The story of collaboration
The project and educational group
«Algorithms of Data Mining for Internet forums on innovative projects» (NRU HSE)
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Crowdsourcing
• From Wikipedia: – Crowdsourcing is a process that
involves outsourcing tasks to a distributed group of people. This process can occur both online and offline (Jeff Howe , 2006)
– Crowdsourcing is related to, but not the same as, human-based computation, which refers to the ways in which humans and computers can work together to solve problems (Quinn & Bederson, 2010)
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Collaborative platform
• Carrying out brainstorming (public examination, crowdsourcing)
• Platform core is a socio-semantic network (users, content)
• Users solve common problem, propose their ideas, evaluate and discuss ideas of each other
• As a result of users and ideas rating we get the best ideas and its generators (best users)
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The goal
The development of special instrument for deeper understanding of collaborative platform users behavior, developing the sufficient rating criteria, dynamics and statistics analysis
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The data analysis scheme
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Formal context: data
• The project «Sberbank-21»: http://sberbank21.ru/
• Objects are platform users
• Attributes are ideas within the topic Sberbank and Private Client
• Object x Attribute datasets:
– The user is the author of the idea
– The user left a comment to the idea or to any of its comments
– The user has evaluated the idea or its comments
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Results: concept lattice
Concept Explorer conexp.sourceforge.net/
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Results: concept lattice
Formal concept: ({User45, User22}, {“Microcredits in [1000, 5000] rub.”})
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Results: “iceberg” lattice
For user-Comment Context for Sberbank-21 Project
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Results: biclustering
BicAT (Biclustering Analysis Toolbox): http://www.tik.ee.ethz.ch/sop/bicat/
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Results: biclustering
Bicluster: ({User1 – User11}, {I1, I2, I3})
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Results: biclustering
Extent Intent Stability Support
Hrabrova_Tatyana_Sergeevna,
Rasul_Gappoev, Alena,
Aleksey_Protsenko,
Valentin_Mashkin,
Aleksandr_Popov,
Maksim_Dubinin,
Mihail_Demchenko,
Dinara_Gorlenko, Viktoriya,
Tatyana_Dmitrova
What_shall_appear_at_physical_
office_of_SB-21?,
A_unique_service_of_2021_for
_small_businesses?,
Sberbank_and_Private_Clients
0,7109375 0,101852
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Results: statistical methods
1
10
100
1 000
1 10 100 1000 10000
Nu
mb
er
of
use
rs
Number of evaluations, x
Distribution evaluation Power Law?
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Power Law Tests
№ Выборка n xmin xmax α p-value
1 Idea generation 64 11 55 3,5 0,73
2.1 Commenting (1) 109 5 681 1,5 0
2.2 Commenting (2) 65 10 199 1,84 0,116
3.1 Evalutation (1) 38 614 5020 3,48 0,78
3.2 Evaluation (2) 70 84 614 1,81 0
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Conclusion
• The developed methodology is useful for collaborative system and system of resource sharing data analysis
• Future work
– Using of textual information
– Applying multimodal clustering methods
– Development of recommender system
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Thank you! Questions?