Adaptive Web Stores

1. Adaptive Web Stores 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

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) Agent 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 Agent Architecture for Personalized Web stores

4. Our goals • customization of product descriptions • presentation of differentsets of features • use of differentlinguistic descriptions to present features • selection of the amount of information to present (to constrain the information load) • suggestion of different items of a product Agent 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 Agent 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) Agent 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: <low, 0.9>,<medium,0.1>,<high,0> • Preferences e.g.: Ease of use: importance: 1; <low, 0>,<medium,0.3>,<high,0.7> Agent Architecture for Personalized Web stores

8. A stereotype (Novice user) • Classification data: age: importance: 0.7; <0-24, 0.3>,<25-44,0.2>, ... job: importance: 0.8; <student, 0.8>,<25-44,0.2>, ... • Personality traits domain expertise: <low, 0.9>,<medium,0.1>,<high,0> technicalinterest : <low, 0.8>,<medium,0.2>,<high,0> receptivity: <low, 0.2>,<medium,0.7>,<high,0.1> • Preferences ease of use: importance: 0.9 <low, 0>,<med,0.2>,<h,0.8> quality: importance: 1; <low, 0>,<med,0.6>,<high,0.3> Agent Architecture for Personalized Web stores

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

10. Page tailored to an expert user Agent Architecture for Personalized Web stores

11. Page tailored to a non-expert user Agent 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) Agent 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 Agent 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 Agent Architecture for Personalized Web stores

15. The System Architecture Usrs DB Mgr Stereotype KB Users DB Personal Agent UM-i UMC W e b S e r v e r Prod Taxonomy Product Extractor Session Mgr Dialog Context Dialog Mgr Extr Context-i Cart Shopping Mgr Products DBMgr ProductsDB Agent Architecture for Personalized Web stores

16. Three-tier architecture II level Solaris JDK 1.1.3 Java Web Server 1.1 I level W e b S e r v e r Browser_i Session Mgr Agents Browser _k Netscape, Ms Explorer Users DB Products DB NT JDK 1.1.4 ODBC driver III level Agent 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 Agent Architecture for Personalized Web stores