presentation slides maya liu

8/3/2019 Presentation Slides Maya Liu

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Web Personalization and

Privacy Concerns

Maya Liu * COMS E6125 * Spring 2011


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What is Web Personalization?

making web pages more relevant to individual userscookies, data logs, recommendation systems, etc

source
http://blog.kissmetrics.com/wp-content/uploads/2010/10/amazon-recommendations.jpg


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Why Personalize?

Saves time and frustration

source
http://www.community-learning.org.uk/webs/40/images/Cartoon4.jpg


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Why Personalize?

Saves resources


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Why Personalize?

boosts e-commerce sales

source
http://c0162861.cdn.cloudfiles.rackspacecloud.com/20100517_amazon.gif


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Current Personalization MethodsWeb-usage mining

source
http://maya.cs.depaul.edu/~mobasher/personalization/architecture.gifhttp://maya.cs.depaul.edu/~mobasher/personalization/architecture.gif


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Current Recommendation Systems

Content-Basedusers and web sites are

modeled as objects with

attributes

think database entries

recs based on user historyand attributesnot useful when there arelarge amounts of

unstructured data

Case-Basedan improvement overcontent-based systemused in e-commerceeliminates unstructureddatarequires same fields forobjects of the same typenot good for objects with

unstructured datapotentially lack diversityin results


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Privacy Concerns

source
http://www.personal.psu.edu/gtk5002/privacy1.gifhttp://www.personal.psu.edu/gtk5002/privacy1.gif


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Privacy Survey, c.2000 source: [8]


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Challenges to Preserving Privacypersonal definitions of privacy differ

regional, cultural, and background differencesregional laws and regulations

source
http://www.toothpastefordinner.com/081208/online-privacy-advocate.gif


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Current Privacy Solutions

Largest permissible dominatorRegion-specific versionsPseudonymous personalization


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Current and Future Improvements

Pseudonymous Personalization

must satisfy the following criteria:unidentifiablelinkable for the personalized system but not thirdpartiesunobservable for third parties

pseudonymous users AND servers


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Current and Future Improvements

Client-side Personalization

store personal data on client computers instead of serversserver leaks pose less risks and potentially increases userwillingness to provide personal datachallenges

aggregate data calculationsrecommendation software must be sent to clients,providing opportunities for reverse-engineering


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Case Study: SUGGEST

"Novel approach to implementing Web personalization as a

single online module"uses "usage clusters" to represent sessionsranks page importance by order of visit from starting pagerelies on aggregate data to weed out outlier dataconstructs directed graph from data points and use forrecommendations

2007, [3]


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Case Study: SUGGESTPros:

stores data in adjacency matrix, and can change itssize and recommendation level based on constraintsno personal data collected, and uses link informationalready provided on many websites

Cons:

restricted to page recommendationsexploitable, similar to Google bombing

2007, [3]


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Future Research Directionsuser anonymitypassive data-gatheringinferring preferences from aggregate data, not specificuser's history

source
http://scienceblogs.com/omnibrain/upload/2006/12/anonymous.jpg


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Questions? Comments?

source
http://farm5.static.flickr.com/4072/4678884792_acfeba9fe5.jpg


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Reference[1] Alfred Kobsa, Privacy-enhanced web personalization, The adaptive web: methods andstrategies of web personalization, Springer-Verlag, Berlin, Heidelberg, 2007[2] Michael J. Pazzani , Daniel Billsus, Content-based recommendation systems, The adaptive web:methods and strategies of web personalization, Springer-Verlag, Berlin, Heidelberg, 2007

[3] Ranieri Baraglia , Fabrizio Silvestri, Dynamic personalization of web sites without userintervention, Communications of the ACM, v.50 n.2, p.63-67, February 2007

[4] Barry Smyth, Case-based recommendation, The adaptive web: methods and strategies of web

personalization, Springer-Verlag, Berlin, Heidelberg, 2007

[5] Magdalini Eirinaki , Michalis Vazirgiannis, Web mining for web personalization, ACMTransactions on Internet Technology (TOIT), v.3 n.1, p.1-27, February 2003

[6] Bamshad Mobasher , Robert Cooley , Jaideep Srivastava, Automatic personalization based onWeb usage mining, Communications of the ACM, v.43 n.8, p.142-151, Aug. 2000

[7] Teltzrow, M. and Kobsa, A.: Impacts of User Privacy Preferences on Personalized Systems: aComparative Study. In: Designing Personalized User Experiences for eCommerce, Karat, C.-M.,Blom, J., and Karat, J., Eds. Dordrecht, Netherlands: Kluwer Academic Publishers (2004) 315-332,DOI 10.1007/1-4020-2148-8_17.

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