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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 email at I hereby grant permission to use them in educational settings. If you do so, it would be nice to send me an email 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., 2004. 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. In National Conference on Artificial Intelligence, pages 403–408. ❖ [Bellekens et al., 2009] Bellekens, P., Houben, G.-J., Aroyo, L., Schaap, K., and Kaptein, A. (2009). User model elicitation and enrichment for context-sensitive personalization in a multiplatform tv environment. In Proceedings of the seventh european conference on European interactive television conference, EuroITV ’09, pages 119–128, New York, NY, USA. ACM. ❖ [Benatallah et al., 2003] Benatallah, B., Casati, F., Toumani, F., and Hamadi, R. (2003). Conceptual mod- eling of web service conversations. In Proceedings of the 15th international conference on Advanced infor- mation systems engineering, CAiSE’03, pages 449–467, Berlin, Heidelberg. Springer-Verlag. ❖ [Coffey et al., 2002] Coffey, J. W., Hoffman, R. R., Can ̃as, A. J., and Ford, K. M. (2002). A Concept Map-Based Knowledge Modeling Approach to Expert Knowledge Sharing. The IASTED International Conference on Information and Knowledge Sharing, pages 212–217. ❖ [Conati et al., 2002] Conati, C., Gertner, A., and Vanlehn, K. (2002). Using Bayesian Networks to Manage Uncertainty in Student Modeling. User Modeling and UserAdapted Interaction, 12(4):371–417. ❖ [Corbett et al., 2000] Corbett, A., McLaughlin, M., and Scarpinatto, K. C. (2000). Modeling student knowl- edge: Cognitive tutors in high school and college. User Modeling and UserAdapted Interaction, 10(2):81– 108. ❖ [Danine et al., 2006] Danine, A., Lefebvre, B., and Mayers, A. (2006). TIDES - Using Bayesian Networks for Student Modeling. ❖ [Delcambre et al., 2008] Delcambre, L., Kaschek, R. H., and Mayr, H. C. (2008). 08181 report – the evolution of conceptual modeling. Technical Report 08181, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany, Dagstuhl, Germany. ❖ [Egusa et al., 2010] Egusa, Y., Saito, H., Takaku, M., Terai, H., Miwa, M., and Kando, N. (2010). Using a concept map to evaluate exploratory search. In Proceeding of the third symposium on Information interaction in context, IIiX ’10, pages 175–184. ACM. ❖ [EUZENAT et al., 2008] EUZENAT, J., PIERSON, J., and RAMPARANY, F. (2008). Dynamic context management for pervasive applications. The Knowledge Engineering Review, 23(Special Issue 01):21–49. ❖ [Gemino and Wand, 2003] Gemino, A. and Wand, Y. (2003). Evaluating modeling techniques based on models of learning. Commun. ACM, 46:79–84. ❖ [Godoy and Amandi, 2005] Godoy, D. and Amandi, A. (2005). User profiling for web page filtering. Internet Computing, IEEE, 9(4):56 – 64. ❖ [Harp et al., 1995] Harp, S. A., Samad, T., and Villano, M. (1995). Modeling Student Knowledge with Self-Organizing Feature Maps. IEEE Trans on Systems Man and Cypernetics, 25:727–737. ❖ [Kaschek, 2008] Kaschek, R. H. (2008). On the evolution of conceptual modeling. In Delcambre, L., Kaschek, R. H., and Mayr, H. C., editors, The Evolution of Conceptual Modeling, number 08181 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany. ❖ [Kay and Lum, 2005] Kay, J. and Lum, A. (2005). Exploiting readily available web data for scrutable student models. In Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pages 338–345, Amsterdam, The Netherlands, The Netherlands. IOS Press. ❖ [Koedinger and Aleven, 1995] Koedinger, K. R. and Aleven, V. (1995). Cognitive Tutors. The Journal of the Learning Sciences, 21(2):2–207. ❖ [Kumar, 2006] Kumar, A. (2006). Using Enhanced Concept Map for Student Modeling in Pogramming Tutors. ❖ [Kumar and Maries, 2007] Kumar, A. and Maries, A. (2007). The effect of open student model on learning: A study. In Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, pages 596–598, Amsterdam, The Netherlands, The Netherlands. IOS Press. ❖ [Li and Li, 2008] Li, Y. and Li, F. (2008). Modeling hierarchical user interests based on hownet and concept mapping. In Semantics, Knowledge and Grid, 2008. SKG ’08. Fourth International Conference on, pages 157 –164. ❖ [Li and Zhong, 2006] Li, Y. and Zhong, N. (2006). Mining ontology for automatically acquiring web user information needs. Knowledge and Data Engineering, IEEE Transactions on, 18(4):554 – 568. ❖ [Limoanco and Sison, 2003] Limoanco, T. and Sison, R. (2003). Learner agents as student modeling: Design and analysis. 3rd Ieee International Conference on Advanced Learning Technologies Proceedings, page 440. ❖ [LUTHER et al., 2008] LUTHER, M., FUKAZAWA, Y., WAGNER, M., and KURAKAKE, S. (2008). Sit- uational reasoning for task-oriented mobile service recommendation. The Knowledge Engineering Review, 23(Special Issue 01):7–19. ❖ [Ma et al., 2008] Ma, H., Schewe, K.-D., Thalheim, B., and Wang, Q. (2008). Composing personalised services on top of abstract state services. In Delcambre, L., Kaschek, R. H., and Mayr, H. C., editors, The Evolution of Conceptual Modeling, number 08181 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany. ❖ [MONTANELLI and CASTANO, 2008] MONTANELLI, S. and CASTANO, S. (2008). Semantically routing queries in peer-based systems: the h-link approach. The Knowledge Engineering Review, 23(Special Issue 01):51–72. ❖ [MYLONAS et al., 2008] MYLONAS, P., VALLET, D., CASTELLS, P., FERNA ́NDEZ, M., and AVRITHIS, Y. (2008). Personalized information retrieval based on context and ontological knowledge. The Knowledge Engineering Review, 23(Special Issue 01):73–100. ❖ [Roussopoulos and Karagiannis, 2009] Roussopoulos, N. and Karagiannis, D. (2009). Conceptual modeling: Foundations and applications. chapter Conceptual Modeling: Past, Present and the Continuum of the Future, pages 139–152. Springer-Verlag, Berlin, Heidelberg. ❖ [SHVAIKO, 2008] SHVAIKO, P. (2008). Guest editorial preface: special issue on contexts and ontologies. The Knowledge Engineering Review, 23(Special Issue 01):1–6. ❖ [Sormo, 2005] Sormo, F. (2005). Case-Based Student Modeling Using Concept Maps. CaseBased Reasoning Research and Development, pages 492–506. ❖ [VEALE and HAO, 2008] VEALE, T. and HAO, Y. (2008). A context-sensitive framework for lexical on- tologies. The Knowledge Engineering Review, 23(Special Issue 01):101–115. ❖ [Villalon and Calvo, 2009] Villalon, J. and Calvo, R. A. (2009). Concept Extraction from student essays, towards Concept Map Mining. In 2009 Ninth IEEE International Conference on Advanced Learning Technologies, 9th IEEE International Conference on Advanced Learning Technologies (ICALT), pages 221–225. IEEE. ❖ [Webb et al., 2001] Webb, G. I., Pazzani, M. J., and Billsus, D. (2001). Machine learning for user modeling. User Modeling and User-Adapted Interaction, 11:19–29.

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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