1. Personalization Speaker: Ping-Tsun Chang 3/7/2002

2. Personalization of WWW10 Designing Personalized Web Applications Designing Personalized Web Applications Session: Personalization in E-Commerce Session: Personalization in E-Commerce Gustavo Rossi, Daniel Schwabe, Robson Guimaraes, Dept. of Informatics, PUC-Rio, Brazil. Gustavo Rossi, Daniel Schwabe, Robson Guimaraes, Dept. of Informatics, PUC-Rio, Brazil. Personalizing Web Sites for Mobile Users Personalizing Web Sites for Mobile Users Session: Content Transformation for Mobility Session: Content Transformation for Mobility Corin R. Anderson, Pedro Domingos, Daniel S. Weld, Department of Computer Science, University of Washington. Corin R. Anderson, Pedro Domingos, Daniel S. Weld, Department of Computer Science, University of Washington.

3. Motivation Different scenrios of personalization covering most existing applications Different scenrios of personalization covering most existing applications Object-Oriented Hypermedia Design Method (OOHDM) Object-Oriented Hypermedia Design Method (OOHDM) Personalized Web applications by refining views according to users’ profiles or preferences Personalized Web applications by refining views according to users’ profiles or preferences

4. Scenrios of Personalization Link Personalization Link Personalization Content Personalization Content Personalization Node structure customization Node structure customization Node content customization Node content customization

5. OOHDM: Conceptual Model Conceptual Model for a CD store Conceptual Model for a CD store Name: String Description: [String+photo] Keywords: {String} Price: Real Size: String Section: {Section} … DeliveryTime: string CD Date: date Order Date: date PaymentMethod Name: String Performer Text: String Comment Name: String Address: … Customer CdDiscount Recommendation

6. OOHDM: Navigation Model Different Navigation Schemata for different profiles Different Navigation Schemata for different profiles Name: String Description: [String+photo] Keywords: {String} Price: Real Size: String Section: {Section} … DeliveryTime: string CD Name: String Performer Text: String Comment Name: String Description: [String+photo] Keywords: {String} Price: Real Size: String Section: {Section} … DeliveryTime: string CD Date: date Order Name: String Address: … User includes boughtByhasComment

7. Hot-spots In the conceptual model: by explicitly representing users, roles and groups and by defining algorithms that implement different (business) rules for different users. In the conceptual model: by explicitly representing users, roles and groups and by defining algorithms that implement different (business) rules for different users. In the navigational model: by defining completely different applications for each profile, by customizing node contents and structure and by personalizing links and indexes. In the navigational model: by defining completely different applications for each profile, by customizing node contents and structure and by personalizing links and indexes. in the interface model: by defining different layouts according to user preferences or selected devices. in the interface model: by defining different layouts according to user preferences or selected devices.

8. Designing Personalized Views Link Personalization Link Personalization Content Personalization Content Personalization Personalizing content in a node Link personalization in OOHDM Link Recommendations, user: Customer SOURCE HomePage TARGET CD:C WHERE C belongsTo user recommendations NODE Customer.CD FROM CD:c, user: Customer Name: String Price: Real [Subject.price – user C Discount ] … Comments: Anchor [Comments] According to some data related with the user’s buying history, his category, etc.

9. Recommendation Customer Recommend Algorithm getRecomm CollaborativerFiltering getRecomm SimpleRecommend getRecomm SpecialRecommend Recommentations() Recommender getRecomm Decoupling users from Recommendation algorithms If we want to improve the use of recommendation algorithms, we can model the assignment of differnet algorithms to different users by using strategies recommender

10. Recommendation: Implement Sequence Diagram for recommendation strategies A LinkA CustomerA RecommAlgorithm recommendations getRecomm A LinkA CustomerThirdParty Adapter recommendations getRecomm ThirdParty Recomm recommInterface Accommodating third party products

11. Context Personalization Navigation Diagram of Conference Paper Review system scenrio Paper by Topic My Reviews by Topic by Reviewer by Author by Paper Review Reviewer Paper Context Specification Card

12. Reusing Specifications Extending a Node Specification for different user profiles NODE CD FROM CD:C Name: String Price: Real Node Customer.CD Extends CD Description: Image Comments: Anchor [Comments] Node Manager.CD Extends CD Comments: Set Select text From Comment: Co Where C hasComment Co

13. Goal of Personalization A Web Personalizer can A Web Personalizer can Make frequently-visited destinations easier to find Make frequently-visited destinations easier to find Highlight content that interests the visitor Highlight content that interests the visitor Elide uninteresting content and structure Elide uninteresting content and structure A Web site personalizer adapts the site for the mobile visitor in a two-step process A Web site personalizer adapts the site for the mobile visitor in a two-step process The personalizer mines the access logs to build a model for each visitor The personalizer mines the access logs to build a model for each visitor The personalizer transforms the site to maximize the expected utility for a given visitor The personalizer transforms the site to maximize the expected utility for a given visitor

14. Personalization for Mobile Users Problem Definition Problem Definition V={v 0,…v m } as m indivial visitors V={v 0,…v m } as m indivial visitors V i =(R, D) a visitor is represented as his history and demographics V i =(R, D) a visitor is represented as his history and demographics R= requests ordered by time R= requests ordered by time r i =(u s, u d, t, c) request is the orginating page, destination page, time, and client r i =(u s, u d, t, c) request is the orginating page, destination page, time, and client D=(d 0,…d n ) demographic information is an n- tuple of data items D=(d 0,…d n ) demographic information is an n- tuple of data items An Evaluation Function F(W, u, v)->R An Evaluation Function F(W, u, v)->R

15. Web Site Model Evaluation Expected Utility Expected Utility F(W, u, v) = E[U v (p)] E[U v (p i )] = E[U v (s i0 )] The excepted utility of a screen is the sum of its intrinsic and extrinsic utilities The excepted utility of a screen is the sum of its intrinsic and extrinsic utilities E[U v (s ij )] = E[IU v (s ij )] + E[EU v (s ij )] Extrinsic utilities measure the value of screen by its connection to the rest of the web site Extrinsic utilities measure the value of screen by its connection to the rest of the web site E[EU v (s ij )] = P(scroll)(E[U v (s i,j+1 )]-r s ) + ∑[P(l ijk )(E[U v (d ijk )]-r l )]

16. Intrinsic Uility intrinsic utility of a screen as a weighted sum of two terms, which related to how the screen’s content matches the intrinsic utility of a screen as a weighted sum of two terms, which related to how the screen’s content matches the visitor’s previously viewed content visitor’s previously viewed content how frequently the visitor viewed the screen. how frequently the visitor viewed the screen. IU v (s ij )] = w sjm. sim V (T ij ) + w freq. freq V (S ij ) sim V (T ij ) = (w Tij. w V )/(||w Tij ||. ||w V ||) Yahoo!