An Agent Architecture For Personalized Web Stores

1. An Agent Architecture For Personalized Web Stores L. Ardissono, C. Barbero, A. Goy, 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 visited by eterogeneous users • IF & EC systems focus on selecting items suitable to the user’s preferences (exploiting techniques like collab. filtering, case-based reasoning, ...) • An interesting expansion is the focus on the interactional aspects on the Web Agent Architecture for Personalized Web stores

3. 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

4. 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

5. 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

6. 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

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

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

9. 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

10. 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

11. 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

12. 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

13. Types of communication among the agents of our system • Synchronous (e.g. to trigger the generation of Web pages) • Asynchronous (e.g. to update the user profiles, or store them on the Users DB) • Multicast (e.g. to trigger the initialization of a user session in all the agents) NB: the agents exchange messages containing complex objects Agent Architecture for Personalized Web stores

14. KQML-like messages Users DB Usrs DB Mgr tell ask-1 insert tell Personalization Agent UMC ask-1 W e b S e r v e r tell ask-1 tell ask-1 ask-all tell tell Session Mgr Product Extractor Dialog Mgr tell tell insert ask-all delete tell tell tell ask-1 Products DBMgr Shopping Mgr ProductsDB Agent Architecture for Personalized Web stores

15. Current Agent-based technologies I • tools for developing multiagent systems (e.g. JAFMAS, JatLite, Voyager) • some offer facilities for interoperability wrt. communication platforms (RMI, CORBA, DCOM) • some support messages based on speech-acts, like KQML-KIF and FIPA-ACL Agent Architecture for Personalized Web stores

16. Current Agent-based technologies II • tools for developing communities of cooperating agent (e.g. COOL) • they offer functionalities to decompose and allocate tasks among agents • tools for building mobile agents (e.g. Aglets, Mole) Agent Architecture for Personalized Web stores

17. Use of Voyager in our system • synchronous, asynchronous and multicast exchange of messages containing complex objects • parallel execution of agents code by means of Java threads • seamless distribution of agents • interoperability through seamless interaction with CORBA-enabled systems Agent Architecture for Personalized Web stores

18. System Architecture Lino Usrs DB Mgr Stereotype KB DB users Canapa UM-i Personaliz Agent UMC W e b S e r v e r Cotone Prot Taxonomy Product Extractor Dialog Context Dialog Mgr SessionMgr Extr Context-i Shopping Mgr Cart ProductsDB ProductsDB Lino Agent Architecture for Personalized Web stores

19. Java Web Server e Servlets Clients Java Web Server Voyager object (DMgr) Servlets HTTP DBMgr HTTP Voyager object RMI JDBC HTTP Voyager object (Prod Extractor) Voyager object Agent Architecture for Personalized Web stores

20. Servlets • Java API that allows to write Web based applications, running within the Web Server • Servlets support: • easy communication with the browser • maintenance of the session dependent data (status) • multi-user access to the Web server • good integration with Voyager Agent Architecture for Personalized Web stores

21. Multiuser access vs. parallelism of execution • Servlets support a multi-user access to the Web server • Voyager supports • parallel user sessions within each agent of the architecture • parallel execution of tasks by asynchronous message invocation Agent Architecture for Personalized Web stores

22. Conclusions • SETA: prototype toolkit for the construction of Web stores tailoring the interaction to the users’ needs • Multiagent system: an agent is associated to each role in the management of the interactions with customers • The agents are distributed over different machines and work in parallel, synchronizing only when necessary Agent Architecture for Personalized Web stores

23. Future work • Enhancement of personalization strategies • Initiative of the system to elicit information about the user’s needs • KQML-like message are a first step towards becoming interoperable with external agents • experiments on a larger number of users Agent Architecture for Personalized Web stores

24. An Agent Architecture For Personalized Web Stores Dipartimento di Informatica Universita’ di Torino, Italy [liliana, giovanna]@di.unito.it http://www.di.unito.it/~seta see a demo of our system on Wed, 10-12:30

25. Representation of user profiles • Classification data: name: Paul; age: 25-44; job: teacher... • Personality traits domain expertise: <low, 0.2>,<medium,0.8>,<high,0> technicalinterest: <low, 0.6>,<medium,0.3>,<high,0.1> receptivity: <low, 0>,<medium,0.2>,<high,0.8> • Preferences ease of use: importance: 0.6; <low, 0>,<medium,0.4>,<high,0.6> quality: importance: 1; <low, 0>,<medium,0.4>,<high,0.6> Agent Architecture for Personalized Web stores

26. Use of stereotypical information • Classification of users: A: importance: ImpA; <v1 , p1>,<v2 , p2>, …, <vn , pn> scoreA = ImpA * pj + (1 - ImpA) (pj is the probability of vj for A in the user profile) Match(scoreA, scoreB) = scoreA * scoreB / (scoreA+scoreB - scoreA*scoreB) • Prediction of user features For each datum, the importance and probabilities of its linguistic values are computed as the weighted sum of the stereotyopical predictions (the weights are the user’s degrees of matching with the stereotypes) Agent Architecture for Personalized Web stores

27. Matching items to users The items to suggest 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

28. Matching items to users • User preferences: A: importance: ImpA; <v1 , p1>,<v2 ,p2>, …, <vn , pn> B: importance: ImpB; <w1 , p1>,<w2 ,p2>, …, <wm , pm> • Item properties: A: vj ; B: wk ; • Degree of matching with the user preference(s) scoreA = ImpA * pj + (1 - ImpA) (pjis the probability of vjfor A in the user profile) Match(scoreA, scoreB) = scoreA * scoreB / (scoreA+scoreB - scoreA*scoreB) Agent Architecture for Personalized Web stores

29. 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

30. Configurability issues • Each agent gets the domain-dependent knowledge from a declarative Knowledge Base (e.g. Stereotype KB) • Friendly configuration tools help the store designer to set up a new virtual store (by introducing the domain-dependent information) Agent Architecture for Personalized Web stores

31. Java Web Server e Servlets Clients Java Web Server DB Server Servlets HTTP RMI HTTP JDBC HTTP Agent Architecture for Personalized Web stores

32. More on the use of Voyager in our system • Java RMI supports only syncronous messages • CORBA supports communication between different languages while our agents are completely written in Java Agent Architecture for Personalized Web stores