• Introduction• The traditional methods of profiling• Web 2.0 – Self profiling• Classifying social networking profiles• Conclusions• References

Personalisation from user profiling

Presented by Jake Daniel CollinsA new approach using web 2.0 data

The traditional methods of profiling

Purchase

history

Profile1

Customer profiles

Marketing campaigns• Direct mail• Telesales scripts• Email newsletters• Website banner ads

Website personalisation (recent)• Recommendations: – films, books, music, games etc• Web content: – news stories, blog posts, forums• Search results

Demo-graphic

data

Web 2.0 – Self profiling

Classifying social networking profiles

P1

{ P1, P2, ..., Pn }

Standardised Profiles

Web 2.0 Unique Profiles

Features

Machine learning

Conclusions

• Not a completely new idea

– consider similar work already done by Google, LastFM, Pandora, Digg and others?

• Future trends

– more people using social networking tools, accessing information online and shopping online

– people beginning to expect personalisation

– Integration with location based content

• Challenges– Addressing concerns over privacy

– Choosing suitable profiles

– Applying machine learning techniques

– Applying natural language processing techniques

References

• Raghu, TS, Kannan, PK, Rao, HR & Whinston, AB 2001, Dynamic profiling of

consumers for customized offerings over the Internet: a model and analysis.

Decision Support Systems, vol. 32, no. 2, pp. 117-134.

• Chellappa, RK & Sin, RG 2005, Personalization versus Privacy: An Empirical

Examination of the Online Consumer's Dilemma. Inf. Technol. and Management,

vol. 6, no. 2-3, pp. 181-202.

• Tan, P, Steinbach, M, Kumar, V 2006 Introduction to Data Mining, Pearson

Education. ISBN-13: 978-0321420527

• Jurafsky, D. & Martin, J.H. 2009 Speech and Language Processing (2nd edition),

Pearson International Edition.

For further information please contact:Jake Daniel Collinsjc317@sussex.ac.ukhttp://techabstractions.wordpress.com