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Introduction The traditional methods of profiling Web 2.0 – Self profiling

Personalisation from user profiling. Introduction The traditional methods of profiling Web 2.0 – Self profiling Classifying social networking profiles Conclusions References. A new approach using web 2.0 data. Presented by Jake Daniel Collins. The traditional methods of profiling.

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Introduction The traditional methods of profiling Web 2.0 – Self profiling

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  1. Personalisation from user profiling • Introduction • The traditional methods of profiling • Web 2.0 – Self profiling • Classifying social networking profiles • Conclusions • References A new approach using web 2.0 data Presented by Jake Daniel Collins

  2. The traditional methods of profiling Demo-graphic data Purchase history 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 Profile 1

  3. Web 2.0 – Self profiling

  4. Classifying social networking profiles Web 2.0 Unique Profiles Standardised Profiles Machine learning Features { P1, P2, ..., Pn} P1

  5. 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

  6. 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 Collins jc317@sussex.ac.uk http://techabstractions.wordpress.com

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