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User Modeling
Franz J. Kurfess
Computer Science Department
California Polytechnic State University
San Luis Obispo, CA, U.S.A.
5© Franz J. Kurfess
Models and Modeling
❖no, we’re not talking about people who present clothes or themselves to an audience
http://en.wikipedia.org/wiki/File:ModelsCatwalk.jpghttp://upload.wikimedia.org/wikipedia/commons/7/7c/Alesya_Nazarova_Model_2009.jpg
http://commons.wikimedia.org/wiki/File:Nuno_Janeiro-Portugal_Fashion.jpg
6© Franz J. Kurfess
Models and Modeling
❖physical model
❖conceptual model
❖causal model
❖data model
❖computer model
❖business model
❖ ...
http://en.wikipedia.org/wiki/File:ModelsCatwalk.jpg
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
http://upload.wikimedia.org/wikipedia/commons/0/0d/Buddelschiff_Titanic.JPG
http://upload.wikimedia.org/wikipedia/commons/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
http://en.wikipedia.org/wiki/File:AIBO_ERS111_210.jpghttp://en.wikipedia.org/wiki/File:Nao_humanoid_robot.jpg
http://en.wikipedia.org/wiki/File:Fozzierowlf.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
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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
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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
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Related Aspects
Modeling and SimulationConceptual Models
Domain ModelsTask Models
Scientific ModelsObject-Oriented Development
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Modeling and Simulation
http://en.wikipedia.org/wiki/Modeling_and_Simulation:_Conceptual_Modeling_Overview
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© 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
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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
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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
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Atmosphere
Composition Model
http://upload.wikimedia.org/wikipedia/commons/9/91/Atmosphere_composition_diagram.jpg
Source
Strategic Plan for the U.S. Climate Change Science Program
, Fig 3.1.
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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
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Simulation
❖ implementation of a model
❖computers are very powerful and flexible simulation platforms
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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
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© 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
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User Profile Aspects
❖categories of information incorporated into user profiles
❖context time, location
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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
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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
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© 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, ...
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Context in User Profiles
❖user preferences
❖domain
❖ task
❖actions context attributes relevant to specific user actions
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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
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Types of User Models
Behavioral ModelAnalytical ModelPredictive Model
Prescriptive ModelAdaptive Model User Prototypes
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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
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Analytical Model
❖combines multiple sources of information about users observation verbalization by users conversation questionnaires knowledge of experts or experienced users
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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
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Prescriptive Model
❖describes permissible actions by the user in a given context used in domains where deviations from prescribed
actions cause serious consequencessafety, security legal issuescompany policies
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Adaptive Model
❖model is continuously updated to reflect changes in the user task, context role behavior knowledge emotional state
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© 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
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© 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
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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
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OpenSocial API
❖set of APIs for building social applications that run on the web http://www.opensocial.org/
❖sharing of social data across Web sites
❖consolidation of user profiles across multiple sites
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Applications
Human-Computer InteractionLearning
AdvertisingRecommendations
Social NetworksSecurity
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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
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Kinect Identity
❖Why user modeling? recognizing and tracking player identity
identify the same player across sessionsdistinguish between multiple players in one sessionsmooth and natural interaction
❖ Identity Tracking Approaches biometric
appearance of the player session
tracking of multiple players
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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
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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 identitiessimilarity scores or distance measures
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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
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