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Implicit Web Personalisation

Implications from the advent of the “implicit web” where users are profiled indirectly and their interactions (e.g. click behaviour) are used as the basis of subsequent marketing.

Table of Contents

Report on ‘implicit web'

Bibliography

Appendix

Technology is constantly evolving and it is changing society along the way. The creation of the Web by Tim Berners-Lee in 1990 has opened a lot of doors and minds as to what computers can be used for.

The implicit web is all about the value that will accrue to an Internet user when their every action is tracked, recorded, and used to provide value back to that user. Overall the Implicit Web is a method of Web personalisation. Magdalini, E. & Michalis, V. (2003) defines web personalisation as the process of customising a web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behaviour (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data.

Implicit does not mean insignificant or unimportant. By contrast, implicit web information is often valuable and even crucial in various situations. For example, implicit information of click rates can help editors decide which online articles or news are the most popular and thus the most popular news judged by the number of clicks would be then listed on the front page. In similar, the same type of implicit click rates can help e-marketers decide which merchandises are among the greatest demanding and in turn the salesman can arrange the next supply line.

The ease and speed with which business transactions can be carried out over the Web have been a key driving force in the rapid growth of e-commerce. Bamshad,M et al (2000) mentions that the ability to track user browsing behaviour down to individual mouse clicks has brought the vendor and end customer closer than ever before. “It is now possible for vendors to personalise their product messages for individual customers on a massive scale, a phenomenon referred to as ‘mass customization' ”. This statement clearly implies the benefit of the information collected by the implicit web to the present online businesses.

One well-known example is Amazon.co.uk, which always lists related buyer recommendations with each of its online merchandise. "Recommendations for you...” (Refer Appendix A) many online customers must be familiar to this label. And more importantly, many web users do care of the content underneath this label. This is a typical example of how implicit web information helps. Amazon is not the only company that benefits from implicit information. In fact, nowadays almost every website that sells something, from groceries to cars, has some back-end mechanism on analysing the traffic (a typical implicit information) and adjusts their sales plan based on the analysis.

Conclusion:-

The implicit web is not part of the Semantic Web, but they are closely related. If the Semantic Web constructs a ‘conceptual model' of World Wide Web, the implicit web constructs a ‘behaviour model' of World Wide Web.

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Bibliography:-

Bamshad, M., Robert, C., & Jaideep, S. (2000) ‘Automatic personalization based on web usage mining', Communications of the ACM [Online] Vol.43 (8), pp. 142-151. Available at: - http://portal.acm.org/citation.cfm?doid=345124.345169 (ACM Portal) [accessed 17th November 2007]

Lita van Wel & Lamber Royakkers. (2004) ‘Ethical issues in web data mining' Ethics and Information Technology [Online] Vol.6 (2), pp.129-140. Available at: - http://portal.acm.org/citation.cfm?id=1031310.1031325&coll=GUIDE&dl=ACM&CFID=15151515&CFTOKEN=6184618 (ACM Portal) [accessed 17th November 2007]

Magdalini, E., & Michalis, V. (2003) ‘Web mining for web personalisation', ACM Transactions on Internet Technology [Online] Vol.3 (1), pp.1-27. Available at: - http://portal.acm.org/citation.cfm?id=643478&dl=GUIDE&coll=GUIDE&CFID=6806629&CFTOKEN=51437610 (ACM Portal) [accessed 17th November 2007]

Steve, F., Kuldeep, K., Mark, M., Susan, D. & Thomas, W. (2005) ‘Evaluating Implicit Measures to Improve Web Search' ACM Transactions on Internet Technology [Online] Vol.23 (2), pp.147-168. Available at: - http://portal.acm.org/citation.cfm?doid=1059981.1059982 (ACM Portal) [accessed 17th November 2007]

Tim Berners-Lee & Mark, F. (2000). ‘Weaving the web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor' New York, USA: HarperCollins Publishers Inc.

Appendix A

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