This document summarizes research on mining data from collaborative platforms. It describes analyzing user behavior on an innovation platform called Sberbank-21 using formal concept analysis, biclustering, and statistical methods. Key results include generating concept lattices to visualize relationships between users and ideas, identifying biclusters of related users and ideas, and finding that some distributions of user evaluations follow power laws. The goal is to better understand platform users and develop rating criteria and recommendation systems.

1. 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

2. The story of collaboration
The project and educational group
«Algorithms of Data Mining for Internet forums
on innovative projects» (NRU HSE)

3. 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)

4. 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)

5. The goal
The development of special instrument for
deeper understanding of collaborative platform
users behavior, developing the sufficient rating
criteria, dynamics and statistics analysis

6. The data analysis scheme

7. 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

8. Results: concept lattice
Concept Explorer
conexp.sourceforge.net/

9. Results: concept lattice
Formal concept:
({User45, User22}, {“Microcredits in [1000, 5000] rub.”})

10. Results: “iceberg” lattice
For user-Comment Context for Sberbank-21 Project

11. Results: biclustering
BicAT (Biclustering Analysis Toolbox): http://www.tik.ee.ethz.ch/sop/bicat/

12. Results: biclustering
Bicluster:
({User1 – User11}, {I1, I2, I3})

13. Results: biclustering
Extent Intent Stability Support
Hrabrova_Tatyana_Sergeevna, What_shall_appear_at_physical_ 0,7109375 0,101852
Rasul_Gappoev, Alena, office_of_SB-21?,
Aleksey_Protsenko, A_unique_service_of_2021_for
Valentin_Mashkin, _small_businesses?,
Aleksandr_Popov, Sberbank_and_Private_Clients
Maksim_Dubinin,
Mihail_Demchenko,
Dinara_Gorlenko, Viktoriya,
Tatyana_Dmitrova

14. Results: statistical methods
1 000
Number of users
100
10
1
1 10 100 1000 10000
Number of evaluations, x
Distribution evaluation
Power Law?

15. 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

16. 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

17. Thank you!
Questions?