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1 User Modeling Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A.

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Presentation on theme: "1 User Modeling Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A."— Presentation transcript:

1 1 User Modeling Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A.

2 2 © Franz J. Kurfess Use and Distribution of these Slides ❖ These slides are primarily intended for the students in classes I teach. In some cases, I only make PDF versions publicly available. If you would like to get a copy of the originals (Apple KeyNote or Microsoft PowerPoint), please contact me via at I hereby grant permission to use them in educational settings. If you do so, it would be nice to send me an about it. If you’re considering using them in a commercial environment, please contact me first.

3 3 Introduction Related Aspects Types of User Models User Modeling Approaches Applications Structure

4 4 Introduction Models and Modeling Purpose of User Modeling

5 5 © Franz J. Kurfess Models and Modeling ❖ no, we’re not talking about people who present clothes or themselves to an audience _Nazarova_Model_2009.jpg Portugal_Fashion.jpg

6 6 © Franz J. Kurfess Models and Modeling ❖ physical model ❖ conceptual model ❖ causal model ❖ data model ❖ computer model ❖ business model ❖...

7 7 © Franz J. Kurfess Physical Model ❖ smaller or larger physical copy of an object  similar in essential characteristics  depends on the modeling purpose  dissimilar in non-essential characteristics  e.g., scale, material, functionality schiff_Titanic.JPG ns/b/b9/Livesteamtrain.jpg

8 8 © Franz J. Kurfess Physical Models of Users ❖ In our context, does it make sense to use physical models of users? ❖ Maybe, but very limited  dummies for potentially dangerous activities  ergonomic models  statues, puppets, marionettes,  humanoid robots d_robot.jpg

9 9 © Franz J. Kurfess Conceptual Model ❖ abstract model of a physical or conceptual entity or system ❖ ambiguous term  model of a concept  model that is conceptual in its nature  preferred interpretation in our context ❖ related terms  mental model  mental image  cognitive model  representation ❖ more on conceptual models later

10 10 © Franz J. Kurfess Conceptual Models of Users ❖ user prototypes (“exemplars”)  representative for categories of users ❖ formal descriptions of users  simulation  verification and validation ❖ digital surrogate  avatar  user agent

11 11 © Franz J. Kurfess Purpose of User Modeling ❖ results of user observation ❖ better understanding of users ❖ experiments  more practical in many respects  error elimination  performance improvement ❖ business and competitive reasons  user and consumer behavior ❖ safety and security ❖ legal aspects

12 12 Related Aspects Modeling and Simulation Conceptual Models Domain Models Task Models Scientific Models Object-Oriented Development

13 13 © Franz J. Kurfess Modeling and Simulation

14 14 © Franz J. Kurfess Conceptual Models ❖ formal description of entities or systems in the real world  may include abstract entities  e.g. society, friendship, nuclear physics, weather patterns, “the cloud” ❖ conveys the fundamental principles, basic functionality, and important properties  formulated such that the intended users can understand it ❖ objectives  enhance the understanding of the system  facilitate communication about the system among stakeholders  reference specification for system designers and developers  documentation

15 15 © Franz J. Kurfess Domain Models

16 16 © Franz J. Kurfess Concepts in a Domain Chapter 6: Creating a Conceptual Model v3.2

17 17 © Franz J. Kurfess Task Models ❖ formal description of a set of activities that together constitute a task  work flow  describes dependencies between the activities  inputs and ouputs  resources  components, materials, consumable  facilities required for the task  actors  people or agents involved in activities that belong to the task  roles  capture distinguishing characteristics of actors with respect to tasks and activities

18 18 © Franz J. Kurfess Scientific Models ❖ generation of abstract models representing empirical objects, phenomena, and processes ❖ often described in a formal modeling language  may depend on the domain  often based on mathematics  examples:  Architecture Description Language (ADL)  Unified Modeling Language (UML)  Virtual Reality Modeling Language (VRML) ❖ basis for simulations  implementations of a model

19 19 © Franz J. Kurfess Atmosphere Composition Model Source Strategic Plan for the U.S. Climate Change Science ProgramStrategic Plan for the U.S. Climate Change Science Program, Fig 3.1.

20 20 © Franz J. Kurfess Object-Oriented Development ❖ often uses similar analysis and modeling techniques ❖ aims at identifying  software components  classes in an OO programming language  functions to implement behaviors ❖ conceptual models  describe real-world entities, systems, concepts  emphasize understanding, not implementation

21 21 © Franz J. Kurfess Simulation ❖ implementation of a model ❖ computers are very powerful and flexible simulation platforms

22 22 User Model Modeling of Human Users User Profiles User Profile Representation

23 23 © Franz J. Kurfess Modeling of Human Users ❖ behavioral model  describes important aspects of activities by human users  often based on observations or recordings of activities ❖ conceptual model  describes the “mind set” of the user  captures internal aspects  requires insights into the mental state of the user

24 24 © Franz J. Kurfess User Profiles ❖ information collected about personal aspects of individual users  observation  activity recording  disclosure by the user ❖ sometimes generalized into aggregate profiles  prototypes, exemplars, categories ❖ human-centric  intended for use by humans ❖ computer-centric  intended for computer programs

25 25 © Franz J. Kurfess User Profile Aspects ❖ categories of information incorporated into user profiles ❖ context  time, location

26 26 © Franz J. Kurfess User Profile Representation ❖ data base  one record per user  schema determines what information is stored ❖ transactions  sets of transactions affiliated with a user  learning techniques may be used to generalize ❖ unstructured text  natural language statements ❖ rules ❖ ontologies  concepts and relationships pertaining to the user

27 27 © Franz J. Kurfess Ontology-Based User Profiles ❖ advantages  semantic aspects facilitate the interpretation of information collected about a user  exchange of user profiles across system boundaries  mapping between different user modeling approaches  reflect the structure of the domain knowledge ❖ problems  creation of ontologies  consideration of dynamic aspects  changes over time

28 28 © Franz J. Kurfess Approaches to Ontology- based User Profiles ❖ weighted concept hierarchy  tree-based structure ❖ reference ontology  similarity or differences with respect to the reference ❖ domain ontology  user preferences and attribute are mapped into the domain ontology ❖ dynamic adaptation  learning techniques to keep track of changes in the user ❖ context  tasks, places, activities, mental state,...

29 29 © Franz J. Kurfess Context in User Profiles ❖ user preferences ❖ domain ❖ task ❖ actions  context attributes relevant to specific user actions

30 30 © Franz J. Kurfess Learning User Profiles ❖ ontology serves as a basis for the user profile  partial ontologies are extracted from a domain ontology  emphasis on relevant aspects for specific tasks or roles ❖ data mining to extract relevant attributes  partial ontologies correspond to concepts shared between attributes  identification of relations between attributes and actions  grouped into larger user contexts  focused on examples with the same or related actions  pruning and summarization to reduce the number of examples

31 31 Types of User Models Behavioral Model Analytical Model Predictive Model Prescriptive Model Adaptive Model User Prototypes

32 32 © Franz J. Kurfess Behavioral Model ❖ based on user observation ❖ captures only observable activities and properties  some aspects may only be observable indirectly  e.g. Internet-based transactions ❖ does not capture aspects internal to the user  intention, motivation, emotional status ❖ often created through user profiling

33 33 © Franz J. Kurfess Analytical Model ❖ combines multiple sources of information about users  observation  verbalization by users  conversation  questionnaires  knowledge of experts or experienced users

34 34 © Franz J. Kurfess Predictive Model ❖ created with the intention of predicting actions of users in specific situations ❖ may be based on or utilize other types of models  behavioral  analytical

35 35 © Franz J. Kurfess Prescriptive Model ❖ describes permissible actions by the user in a given context  used in domains where deviations from prescribed actions cause serious consequences  safety, security  legal issues  company policies

36 36 © Franz J. Kurfess Adaptive Model ❖ model is continuously updated to reflect changes in the user  task, context  role  behavior  knowledge  emotional state

37 37 © Franz J. Kurfess User Prototypes ❖ set of “typical” users that represent user categories  often easier to specify than one complex model for all user categories

38 38 User Modeling Approaches User Profiling

39 39 © Franz J. Kurfess User Profile ❖ captures essential information about individual users  activities  choices and decisions ❖ often in collaboration with users  user preferences solicited from the user  traceable activities  “preferred customer” programs ❖ sometimes without the knowledge of the user  mobile phone records  cookies  license plate tracking

40 40 © Franz J. Kurfess User Profile and User Model ❖ a user profile contains information about an individual user  digital representation of a person’s identity ❖ a user model is an abstract specification of user characteristics  usually not tied to individual users ❖ similar to the distinction between class and instance in object-oriented modeling ❖ however, the terminology is still evolving  no commonly agreed-upon definition  user profile and user model are sometimes used interchangeably

41 41 © Franz J. Kurfess OpenSocial API ❖ set of APIs for building social applications that run on the web  ❖ sharing of social data across Web sites ❖ consolidation of user profiles across multiple sites

42 42 Applications Human-Computer Interaction Learning Advertising Recommendations Social Networks Security

43 43 © Franz J. Kurfess Human-Computer Interaction ❖ Kinect Identity: User Profiles in Microsoft’s Kinect / Xbox 360  Leyvand T, Meekhof C, Wei Y, Sun J, Guo B (april 2011) Kinect Identity: Technology and Experience. Computer 44(4):94 -96

44 44 © Franz J. Kurfess Kinect Identity ❖ Why user modeling?  recognizing and tracking player identity  identify the same player across sessions  distinguish between multiple players in one session  smooth and natural interaction ❖ Identity Tracking Approaches  biometric  appearance of the player  session  tracking of multiple players

45 45 © Franz J. Kurfess Identity Tracking Techniques ❖ multiple techniques are combined  robust  limited impact on CPU and memory  independent of each other ❖ many experimental techniques evaluated ❖ final set  face recognition  clothing color tracking  height estimation

46 46 © Franz J. Kurfess Facial Recognition User Profile ❖ match between the stored user profile and information extracted from the current input  location and size of the face in the image  “facial signature”  normalization  comparison against a data base of stored normalized facial signatures with affiliated identities  similarity scores or distance measures

47 47 © Franz J. Kurfess Facial Matching Example  Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (December 2003) Face recognition: A literature survey. ACM Comput. Surv. 35:399–458 Papatheodorou and Rueckert, 2004 Papatheodorou, T., Rueckert, D., Evaluation of automatic 4D face recognition using surface and texture registration. In: Proc. Sixth IEEE Internat. Conf. on Automatic Face and Gesture Recognition Seoul, Korea, May, pp. 321–326. Color-based representation of residual 3D distances (a) from two different subjects and (b) from the same subject

48 48 © Franz J. Kurfess Learning ❖ student profiles for customization of learning materials

49 49 © Franz J. Kurfess Advertising

50 50 © Franz J. Kurfess Recommendations

51 51 © Franz J. Kurfess Social Networks

52 52 © Franz J. Kurfess Security

53 53 Conclusions

54 54 © Franz J. Kurfess Summary User Modeling

55 55 References

56 56 © Franz J. Kurfess References ❖ [Ahn et al., 2007] Ahn, J.-w., Brusilovsky, P., Grady, J., He, D., and Syn, S. Y. (2007). Open user profiles for adaptive news systems: help or harm? In Proceedings of the 16th international conference on World Wide Web, WWW ’07, pages 11– 20, New York, NY, USA. ACM. ❖ [Amershi and Conati, 2007] Amershi, S. and Conati, C. (2007). Unsupervised and supervised machine learn- ing in user modeling for intelligent learning environments. In Proceedings of the 12th international con- ference on Intelligent user interfaces, IUI ’07, pages 72–81, New York, NY, USA. ACM. ❖ [Aroyo and Houben, 2010] Aroyo, L. and Houben, G.-J. (2010). User modeling and adaptive semantic web. Semantic Web, 1(1):105–110. ❖ [Baffes and Mooney, 1996] Baffes, P. T. and Mooney, R. J. (1996). A Novel Application of Theory Refine- ment to Student Modeling. 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57 57 Ausklang

58 58 © Franz J. Kurfess Post-Test

59 59 © Franz J. Kurfess Evaluation ❖ Criteria

60 60 © Franz J. Kurfess  Click to edit Master text styles Important Concepts and Terms ❖ conceptual model ❖ user model

61 61 © Franz J. Kurfess

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