1. L. Ardissono, C. Barbero, A. Goy and G. Petrone Dipartimento di Informatica Universita’ di Torino, Torino, Italy [liliana,cris,goy,giovanna]@di.unito.it http://www.di.unito.it/~seta Adaptive Web Stores

2. May 4, 1999Agent Architecture for Personalized Web stores 2 The problem electronic catalogs are difficult to browse  they often contain very different types of information, or are not detailed enough  eterogeneous people visit them  people have different interests, backgrounds, interaction needs  there is no single solution to satisfy all needs (see also Benyon:93, Smith-etal:97)

3. May 4, 1999Agent Architecture for Personalized Web stores 3 An improvement...  Information Filtering & Electronic Commerce systems focus on selecting items suitable to the user’s preferences (exploiting techniques like collaborative filtering, case-based reasoning,...)  An interesting expansion is the focus on the interactional aspects on the Web

4. May 4, 1999Agent Architecture for Personalized Web stores 4 Our goals customization of product descriptions –presentation of different sets of features –use of different linguistic descriptions to present features –selection of the amount of information to present (to constrain the information load) suggestion of different items of a product

5. May 4, 1999Agent Architecture for Personalized Web stores 5 Personalization strategies in SETA To generate the pages our system –identifies the user preferences and interests –tailors the contents of the catalog pages to the user characteristics –suggests the items best matching the preferences in the user profile

6. May 4, 1999Agent Architecture for Personalized Web stores 6 Relevant areas dynamic hypermedia (to generate Web pages ‘on the fly’) user modeling (to handle user profiles) knowledge-based systems (to handle the information about products and customers) distributed agent architectures (to exploit specialized agents within a complex system)

7. May 4, 1999Agent Architecture for Personalized Web stores 7 Representation of user profiles Classification data (age, job, …) Personality traits (domain expertise, technical interest, aesthetic interest, receptivity) e.g.: Domain Expertise :,, Preferences e.g.: Ease of use : importance: 1;,,

8. May 4, 1999Agent Architecture for Personalized Web stores 8 A stereotype (Novice user) Classification data:Classification data: age: importance: 0.7;,,... job: importance: 0.8;,,... Personality traitsPersonality traits domain expertise:,, technical interest :,, receptivity:,, PreferencesPreferences ease of use: importance: 0.9,, quality: importance: 1;,,

9. May 4, 1999Agent Architecture for Personalized Web stores 9 Representation of items VivaVoce T200 FeaturesFeatures agenda:20 numbers price: Lit. 90.000 PropertiesProperties ease of use: high quality: high Link to database tableLink to database table NB: the Features are typed slots (there are technical, aestetic features, etc.)

10. May 4, 1999Agent Architecture for Personalized Web stores 10 Page tailored to an expert user

11. May 4, 1999Agent Architecture for Personalized Web stores 11 Page tailored to a non-expert user

12. May 4, 1999Agent Architecture for Personalized Web stores 12 Key roles in the architecture I Communication with the Web (SessionMgr) Management of the interaction flow (DialogMgr) Generation of the catalog pages by applying personalization strategies (Personalization agent) Initialization and update of user profiles by applying user modeling acquisition rules (UMC)

13. May 4, 1999Agent Architecture for Personalized Web stores 13 Key roles in the architecture II Selection and rating of the items to suggest to the user (Product Extractor) Management of the Users DB (to maintain user profiles in a permanent way) Management of the Products DB (containing the information about items) Maintenance of the user’s shopping cart

14. May 4, 1999Agent Architecture for Personalized Web stores 14 Matching items to users the items to be suggested are scored on the basis of the preferences in the user profile the property values of each item are matched against the user’s preferences, to identify the best matching items in the scoring process, the importance of the user’s preferences is exploited to rule out irrelevant mismatching properties

15. May 4, 1999Agent Architecture for Personalized Web stores 15 WebServerWebServer Usrs DB Mgr Users DB UMC Personal Agent Dialog Mgr Product Extractor Session Mgr Shopping Mgr ProductsDB Products DBMgr Stereotype KB UM-i Cart Extr Context-i Dialog Context The System Architecture Prod Taxonomy

16. May 4, 1999Agent Architecture for Personalized Web stores 16 Netscape, Ms Explorer III level WebServerWebServer Users DB Session Mgr Products DB Agents Browser_i Browser _k Solaris JDK 1.1.3 Java Web Server 1.1 NT JDK 1.1.4 ODBC driver II level I level Three-tier architecture

17. May 4, 1999Agent Architecture for Personalized Web stores 17 Conclusions SETA: virtual store shell for the construction of Web stores capable of tailoring the interaction to the users’ needs Agent-based system, where agents have been associated to each basic role in the management of the interactions with customers Special attention has been posed on user modeling and personalization strategies