1. Generic User Modeling Systems Alfred Kobsa Presenter: Michael V. Yudelson

2. Michael V. Yudelson (C) 20052 Roadmap 1. Definition 2. Academic (classic) GUMS Functionality, Requirements, Examples 3. Commercial GUMS Functionality, Requirements, Examples 4. Future of GUMS 5. Advanced Examples

3. Michael V. Yudelson (C) 20053 Definition Generic/General User Modeling Systems (GUMS) † AKA User Modeling Shell Systems Generic/general – i.e. application independent. Configured at development time, filled with specific user data and queried at run time. † Term coined by Tim Finin in Finin, T. W. (1989), GUMS: A general user modeling shell. In: A. Kobsa and W. Wahlster (eds.), User Models in Dialog Systems. Springer-Verlag, Berlin, Heidelberg, pp. 411-430, 1989

4. Michael V. Yudelson (C) 20054 Roadmap 1. Definition 2.Academic (classic) GUMS Functionality, Requirements, Examples 3. Commercial GUMS Functionality, Requirements, Examples 4. Future of GUMS 5. Advanced Examples

5. Michael V. Yudelson (C) 20055 Academic (classic) GUMS Early 90-ies Inherit from user-adaptive systems Structure and Process components choice  Intuition and experience based

6. Michael V. Yudelson (C) 20056 Functionality if Academic GUMS(1)  Represent assumptions about individual characteristics (knowledge, goals, plans)  Represent assumptions about group characteristics – stereotypes (expert, novice) Classifying users into stereotypes  Storing users’ history interaction with the system Forming assumptions about user based on his/her past interaction with the systems

7. Michael V. Yudelson (C) 20057 Functionality if Academic GUMS(2) Generalization of user interaction histories into stereotypes Inferring additional assumptions on the initial ones Maintaining consistency of the model Evaluation of the entries in user model and comparison with standards All listed services are all ‘observational’

8. Michael V. Yudelson (C) 20058 Requirements of Academic GUMS (Classical requirements) Generality and domain independence  Usable in as many applications and domains as possible and provide as many services as possible Expressiveness  Express as many assumptions about user as possible Strong inferential capabilities  Perform various types of reasoning and conflict resolution

9. Michael V. Yudelson (C) 20059 Observations on Academic GUMS Domain independence is often violated (at the cost of generality)  Adaptive learning environments (Brusilovsky)  User-tailored web sites (Kobsa)  Complex capabilities are becoming redundant Almost all GUMS are ‘mentalistic’  Model propositions (goals, plans, knowledge), behavior is an information source  Very few detect behavior patterns (e.g. LaboUr, DOPPELGÄNGER)

10. Michael V. Yudelson (C) 200510 Examples of Academic GUMS (1) BGP-MS (Kobsa and Pohl, 95; Pohl, 98)  Assumptions about user and user groups  Assumptions represented in first order predicate logic  Subset of assumptions are stored as terminological logic  Inferences across multiple types of assumptions (i.e. types of modals)  Deployed as a server with multi-user and multi-application capabilities

11. Michael V. Yudelson (C) 200511 Examples of Academic GUMS (2) DOPPELGÄNGER (Orwant, 95)  Accepts information from software and hardware sensors  Generalizing and extrapolating data from sensors linear prediction Markov models unsupervised clustering for stereotypes  Scrutabile and open user model (inspectable and modifiable)

12. Michael V. Yudelson (C) 200512 Examples of Academic GUMS (3) um (Kay, 95) – a toolkit for user modeling  Stores assumptions about user characteristics Knowledge, beliefs, preferences  Stores as attribute-value pairs  Each piece of information has a list of evidence for its truth or falsehood  The source of each piece of evidence is also recorded

13. Michael V. Yudelson (C) 200513 Roadmap 1. Definition 2. Academic (classic) GUMS Functionality, Requirements, Examples 3.Commercial GUMS Functionality, Requirements, Examples 4. Future of GUMS 5. Advanced Examples

14. Michael V. Yudelson (C) 200514 Commercial GUMS Personalization paradigm  Individualized delivery of promotions, news, ads, and services Shift to a “one-to-one” marketing in e- commerce

15. Michael V. Yudelson (C) 200515 Characteristics if Commercial GUMS User information is stored in an integrated repository shared by multiple applications User information acquired by one system can be employed by others Information about user is stored in non- redundant manner User stereotypes are set a priori Methods and tools for security, identification, and access control are actively used User information

16. Michael V. Yudelson (C) 200516 Requirements of Commercial GUMS Comparing different user actions  Matching definitive actions (purchases of certain items) to vague concepts: taste, personality, lifestyle. AKA Collaborative filtering Import of external user information  Broad variety of user data and data formats require interfaces allowing integration at a reasonable cost Privacy support

17. Michael V. Yudelson (C) 200517 Observations on Commercial GUMS Very behavior-oriented  Action patterns lead directly to adaptation without explicit representation (e.g. via goals, plans) Rate poorly on requirements of Academic GUMS  Generality, expressiveness, inference) Quite domain dependant  Used for limited personalization purposes

18. Michael V. Yudelson (C) 200518 Classical GUMS Requirements revised for Commercial GUMS (1) Quick adaptation  E-commerce web application require adaptation after a short term of interaction. Methods vary depending on amount of information at hand Extensibility  Strong data and process integration capabilities are required Load balancing  Servicing high volumes of users without degradation of quality

19. Michael V. Yudelson (C) 200519 Classical GUMS Requirements revised for Commercial GUMS (2) Failover strategies  Fallback (rollback) mechanisms in case of a breakdown Transactional Consistency  Parallel read/writes of assumptions about user  Inconsistency resolution

20. Michael V. Yudelson (C) 200520 Examples of Commercial GUMS (1) Group Lens (Net Perceptions 2000)  Collaborative filtering to determine user interests  Collects explicit ratings (online forms), and  Implicit ratings, derived from navigation Products user reviewed Products user added to the shopping cart Products purchased

21. Michael V. Yudelson (C) 200521 Examples of Commercial GUMS (2) Personalization Server (ATG 2000)  Multiple rules for classifying user into stereotypes  User data: demographic, system usage, user software and network environment  Rules for inferring individual assumptions from user behavior  Target: personalization of web-page content  Hard-core stereotype approach (Rich)

22. Michael V. Yudelson (C) 200522 Examples of Commercial GUMS (3) Learn Sesame (Open Sesame, 2000)  Domain model: objects, attributes and events  Categorizes incoming information (from an application) according to domain model  Incremental clustering: detects Recurring patterns Similarity Correlations  Interesting observations are reported back

23. Michael V. Yudelson (C) 200523 Roadmap 1. Definition 2. Academic (classic) GUMS Functionality, Requirements, Examples 3. Commercial GUMS Functionality, Requirements, Examples 4. Future of GUMS 5. Advanced Examples

24. Michael V. Yudelson (C) 200524 Future trends of GUMS Mobile GUMS  Agents residing on mobile devices.  Bandwidth and computation power limitation GUMS for smart appliances  E.g. Car/engine lock and wheel/sit/mirrors settings in smart card/chip  E.g. Accelerator/Gear chip in Automobile

25. Michael V. Yudelson (C) 200525 Roadmap 1. Definition 2. Academic (classic) GUMS Functionality, Requirements, Examples 3. Commercial GUMS Functionality, Requirements, Examples 4. Future of GUMS 5. Advanced Examples

26. Michael V. Yudelson (C) 200526 Advanced Examples A. Unified User Context Model (UUCM) – Niederée et al B. Personis – Kay et al

27. Michael V. Yudelson (C) 200527 Advanced Examples – UUCM (1) Unified User Context Model (UUCM)  Contexts – capture user behavior relevant to different situations  Dimensions – address various approaches to personalization UM Levels  Abstract – meta-ontology of: Contexts, facets, facet properties, model dimensions  Concrete – extended ontology of: Dimensions and facets

28. Michael V. Yudelson (C) 200528 Advanced Examples – UUCM (2) User model – consists of (1-M)  Contexts – described by (N-M) Facets – belong to one or many (N-1)  Dimensions, like  Task dimension (Current task f., Task history f.)  Relationship dimension ()  Cognitive pattern dimension (competence f., preference f.)  Environment dimension (Time f., Location f., Device f.) Abstract, Concrete

29. Michael V. Yudelson (C) 200529 Advanced Examples – UUCM (3) Context – Passport presented to a system  Groups of facets relevant to particular role user plays – Working Contexts (UM design time conf.) As user role changes – different WC is used  Subsets of the facet groups used by a particular system – Context-of-Use (IS run time invocation) System might not address all the facets of the context UM Context – IS interaction  IS partially interprets Working Context (In)  IS updates Contexts to reflect interaction with user

30. Michael V. Yudelson (C) 200530 Advanced Examples – UUCM (4) Summary  UUCM – Framework for cross-system deployment of multi-dimensional UM UM defined as full as possible IS read and write from/to part of it  Protocols are yet to be developed

31. Michael V. Yudelson (C) 200531 Advanced Examples A. Unified User Context Model (UUCM) – Niederée et al B. Personis – Kay et al

32. Michael V. Yudelson (C) 200532 Advanced Examples – Personis (1) Main paradigm – scrutinized UM  User can review his/her UM  User can change his/her UM Environment components  UM Server  Direct (generic) scrutiny interface  Multiple Adaptive Systems  Multiple Views/Filters between UM Server and Adaptive Systems (AS scrutiny interface)

33. Michael V. Yudelson (C) 200533 Advanced Examples – Personis (2) User control over the model  View/Change UM  Allow Adaptive Systems to control parts of his/her UM Parameters, values, source of information

34. Michael V. Yudelson (C) 200534 Advanced Examples – Personis (3) Personis Internal Architecture  OODB accessible through  User Model Server  Management interface  Adaptive Systems implemented as UMS Clients Each AS deploys resolvers on UMS – interpreters of the low-level evidence Implementation  XML RPC

35. Michael V. Yudelson (C) 200535 Advanced Examples – Personis (4) Language of Personis (abstraction)  Connecting to UMS  um=access(odbname,login,password)  Requesting information  componenets=um.ask(context,view,resolver_id)  Writing information  tell(context, component, evidence)

36. Michael V. Yudelson (C) 200536 References Kobsa, A. (2001) Generic User Modeling Systems. User Modeling and User-Adapted Interaction. 11, 49-63, Kluwer, 2001 Niederée, C., Stewart, A., Mehta, B. and Hemmje, M. (2004) A Multi-Dimensional, Unified User Model for Cross-System Personalization. In: Proceedings of the AVI 2004 Workshop On Environments For Personalized Information Access, 2004. Kay, J., Kummerfeld, B., Lauder, P. (2002) Personis: A server for user models. In: AH’02: Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems, Springer-Verlag, London, UK, 2002

37. Michael V. Yudelson (C) 200537 Questions…