Post on 07-Apr-2018
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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
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Why Personalize?
Saves time and frustration
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Why Personalize?
Saves resources
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Why Personalize?
boosts e-commerce sales
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Current Personalization MethodsWeb-usage mining
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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
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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
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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
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Questions? Comments?
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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.