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1 User Modeling Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A.
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 Introduction Related Aspects Types of User Models User Modeling Approaches Applications Structure
4 Introduction Models and Modeling Purpose of User Modeling
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 © Franz J. Kurfess Models and Modeling ❖ physical model ❖ conceptual model ❖ causal model ❖ data model ❖ computer model ❖ business model ❖...
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 © 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 © 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 © 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 © 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 Related Aspects Modeling and Simulation Conceptual Models Domain Models Task Models Scientific Models Object-Oriented Development
13 © Franz J. Kurfess Modeling and Simulation
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 © Franz J. Kurfess Domain Models
16 © Franz J. Kurfess Concepts in a Domain Chapter 6: Creating a Conceptual Model v3.2
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 © 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 © 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 © 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 © Franz J. Kurfess Simulation ❖ implementation of a model ❖ computers are very powerful and flexible simulation platforms
22 User Model Modeling of Human Users User Profiles User Profile Representation
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 © 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 © Franz J. Kurfess User Profile Aspects ❖ categories of information incorporated into user profiles ❖ context time, location
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 © 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 © 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 © Franz J. Kurfess Context in User Profiles ❖ user preferences ❖ domain ❖ task ❖ actions context attributes relevant to specific user actions
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 Types of User Models Behavioral Model Analytical Model Predictive Model Prescriptive Model Adaptive Model User Prototypes
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 © 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 © 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 © 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 © Franz J. Kurfess Adaptive Model ❖ model is continuously updated to reflect changes in the user task, context role behavior knowledge emotional state
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 User Modeling Approaches User Profiling
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 © 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 © 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 Applications Human-Computer Interaction Learning Advertising Recommendations Social Networks Security
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 © 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 © 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 © 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 © 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 © Franz J. Kurfess Learning ❖ student profiles for customization of learning materials
49 © Franz J. Kurfess Advertising
50 © Franz J. Kurfess Recommendations
51 © Franz J. Kurfess Social Networks
52 © Franz J. Kurfess Security
54 © Franz J. Kurfess Summary User Modeling
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. 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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. 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58 © Franz J. Kurfess Post-Test
59 © Franz J. Kurfess Evaluation ❖ Criteria
60 © Franz J. Kurfess Click to edit Master text styles Important Concepts and Terms ❖ conceptual model ❖ user model
61 © Franz J. Kurfess
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