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

The story of collaboration

The project and educational group

«Algorithms of Data Mining for Internet forums on innovative projects» (NRU HSE)

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)

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)

The goal

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

The data analysis scheme

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

Results: concept lattice

Concept Explorer conexp.sourceforge.net/

Results: concept lattice

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

Results: “iceberg” lattice

For user-Comment Context for Sberbank-21 Project

Results: biclustering

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

Results: biclustering

Bicluster: ({User1 – User11}, {I1, I2, I3})

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

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?

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

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

Thank you! Questions?