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User Modeling

Franz J. Kurfess

Computer Science Department

California Polytechnic State University

San Luis Obispo, CA, U.S.A.

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IntroductionRelated Aspects

Types of User ModelsUser Modeling Approaches

Applications

Structure

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Introduction

Models and ModelingPurpose of User Modeling

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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

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Models and Modeling

❖physical model

❖conceptual model

❖causal model

❖data model

❖computer model

❖business model

❖ ...

http://en.wikipedia.org/wiki/File:ModelsCatwalk.jpg

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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

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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

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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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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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Domain Models

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Concepts in a Domain

Chapter 6: Creating a Conceptual Model v3.2

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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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User Model

Modeling of Human UsersUser Profiles

User Profile Representation

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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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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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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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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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User Modeling Approaches

User Profiling

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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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Learning

❖student profiles for customization of learning materials

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Advertising

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Recommendations

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Social Networks

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Security

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Conclusions

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