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ΕΠΛ 435: Αλληλεπίδραση Ανθρώπου Υπολογιστή. User Modeling Adaptation Personalization. Based on lectures given by Dr. Vania Dimitrova , University of Leeds, UK. Can Human-Computer interaction become personalised? Adding personalisation features in computer systems. The Personalisation Buzz.

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ΕΠΛ 435:Αλληλεπίδραση Ανθρώπου Υπολογιστή

User Modeling Adaptation

Personalization

Based on lectures given by Dr. VaniaDimitrova, University of Leeds, UK

the personalisation buzz

Can Human-Computer interaction become personalised?

Adding personalisation features in computer systems

The Personalisation Buzz
  • We want information and servicesto be tailored to what we need, what we want, what best fits
  • our current state and purpose.
  • Personalised Human-Human interaction:
  • Route direction
  • Finding places to visit
  • Buying new car
  • Finding people to get in touch with
  • ……

http://www.personalizationmall.com/

broad definition of personalisation
Broad Definition of Personalisation
  • “Whenever something is modified in its configuration or behaviour by information about the user, this is personalisation.”(Searby, 2003)
  • One size does not fit all!
    • delivering the right information
    • to the right person
    • at the right time
    • in the right way
today

Have you come across any personalisation functionality?

Today
  • “Businesses are no longer asking if they should do personalisation it is simply a matter of when and how” (Larsen, 2000)
  • E-commerce (e.g. Amazon, eBay)
  • Search (e.g. Google, Yahoo, Altavista)
  • Desktops (Microsoft)
  • Mobile devices (e.g. Vodafone, Siemens)

Key industries are adopting personalisation

why do we need personalisation
Why Do We Need Personalisation?
  • Available information
    • Abundance
    • Heterogeneity
    • Noise
    • Constraints (bandwidth, time)
  • People
    • Capabilities and background
    • Task, goal, intentions
    • Context
    • Affective states
two types of personalisation
Two Types of Personalisation

Adaptable (customisable) systems

  • U is able to modify aspects of S to suit his/her own preferences

Adaptive systems

  • S modifies its own behavior at least partly independently of specifications by U

Intermediate cases

  • S proposes possible adaptations, U decides which ones to accept
knowledge management personalisation

What are the main advantages of personalising google search?

Are there any possible drawbacks?

Knowledge Management & Personalisation
  • Bryan Horling & Robby Bryant, Google engineers

Personalised search with Google

http://www.youtube.com/watch?v=EKuG2M6R4VM

technologies of tomorrow

Will personalisation play a role in tomorrow’s technology?

Technologies of Tomorrow
  • What will be the technologies in 2015?
  • And in 2020?

http://www.ted.com/

  • The Sixth Sense

http://www.ted.com/talks/lang/eng/pattie_maes_demos_the_sixth_sense.html

example user adaptable interface
Example: User-adaptable Interface

Customise your web browser

my.yahoo.com

example purchase recommendations
Example: Purchase Recommendations
  • Too much content
  • Too many choices
example personalised assistants
Example: Personalised Assistants

Humanising the digital experience(http://www.soliloquy.com/solutions/notebook_demo.php)

  • Adaptive dialogue
  • Recognising user interests
  • Recommending relevant items
example personalised car interface
Example: Personalised Car Interface

Receive the right service at the right time in the right way

  • Cognitive factors
  • Affective factors
  • User task, goal
  • Context awareness
in class exercise

How can the BBC news site be changed to become personalised?

In class Exercise

Question: Assume you use the BBC web page to read news. It behaves in the same way for every user.

knowledge management and personalisation

Is Personalization the Holy Grail of Email Marketing?

Deb Daufeldt is Founder & President of Second Story Solutions, LLC.

Ezine@Articles, January 13, 2010

http://ezinearticles.com/?Is-Personalization-the-Holy-Grail-of-Email-Marketing?&id=3573208

How can personalisation be integrated in email systems and what is the added value?

What is the connection between personalisation and taming the data?

Knowledge Management and Personalisation

User experience - Taming the Data Tiger

schema of user adaptive systems

USER MODEL

USER MODELAPPLICATION

USER MODELACQUISITION

INFORMATION ABOUT U

ADAPTING TO U

Schema of User-Adaptive Systems
main definitions
Main Definitions
  • User Model – data structure that contains explicit assumptions on all aspects of U that are relevant to the adaptive behaviour of S
  • User Model Acquisition – procedure that incrementally construct the user model and whose functions are to:
    • storing, update, and delete entries in the user model;
    • maintain user model consistency;
    • ensure user model validity.
  • User Model Application – procedure that uses the user model to:
    • make predictions about U;
    • take decisions about how to adapt to U.
example 1 smart menus

U’s MENU OPTIONS

REASON ABOUTU’s OPTIONS

REGISTERINGMENU SELECTION

U’s MENU SELECTION

DECIDE MENU CONTENT

Example 1: Smart Menus
example 2 user adaptive java tutorial

U’s PROFILEU’s KNOWLEDGE MODEL

REASON ABOUTU’s KNOWLEDGE & PREFERENCES

EXTRACTING INFO ABOUT U’s KNOWLEDGE AND PREFERENCES

U’s BROWSING BEHAVIOUR U’s TEST PERFORMANCE

GIVE HELP & FEEDBACKSUGGEST READING EXERCISES

Example 2: User-adaptive Java Tutorial
functions of user adaptive systems
Functions of User-adaptive Systems

Supporting system use

  • Taking over parts of routine tasks(e.g. I-EMS – intelligent email sorting system)
  • Adapting the interface(e.g. Smart menus; adaptation for disable users)
  • Helping with system use(e.g. DiamondHelp – wizard helping with collaboration)
functions of user adaptive systems cont
Functions of User-adaptive Systems (cont.)

Supporting system use (cont.)

  • Mediating interaction with the real world(e.g. Lilsys – user’s availability for communication)
  • Controlling a dialogue(e.g. Kyoto City bus information system)
functions of user adaptive systems cont1
Functions of User-adaptive Systems (cont.)

Supporting information acquisition

  • Helping users to find information(e.g. Google’s personalised search)
  • Recommending products(e.g. Amazon; Active Buyers Guide)
  • Tailoring information presentation(e.g. RIA – helping users search for real estates)
functions of user adaptive systems cont2
Functions of User-adaptive Systems (cont.)

Supporting information acquisition (cont.)

  • Supporting collaboration(e.g. Agent Salon – finding people to connect with)
  • Supporting learning(e.g. SQL tutor – adaptive feedback)
adaptivity the sixth sense technology
Adaptivity & the Sixth Sense Technology

Technologies of tomorrow: ubiquitous

Example: The Sixth Sense

http://www.ted.com/talks/lang/eng/pattie_maes_demos_the_sixth_sense.html

What adaptive features can be added to the sixth sense technology?

How will this be done? - Draw the architecture to show the main components.

adaptivity interaction design

Re(Design)

Build an interactive version

Evaluate

Identify needs and establish requirements

Who are the users?

What differences matter?

What adaptive features are required?

Adaptivity & Interaction Design

Identify criteria to judge the effectiveness of adaptivity - improved user satisfaction, increased sales, increased learning, efficiency, less errors, etc.

Adaptive features often added features, how will this affect the existing design?

Final

Product

Build high-fidelity prototypes to demonstrate adaptive features.

recommender systems intro
Recommender systems: intro
  • The problem:
    • too much content!
    • too many choices!
how do recommender systems work
How do recommender systems work?

Major types of algorithms

  • Collaborative or social filtering
    • Suggestion lists, “top-n” offers and promotions
  • Content-based recommenders
    • E-mail filters, clipping services
  • Hybrid recommenders
    • Suggestion lists, “top-n” offers and promotions
collaborative filtering
Collaborative filtering
  • Other’s ratings
    • What others like
  • My ratings
    • What I think like

Give me what people similar to me would like

“Word of mouth”

“Voting”

content based filtering
Content-based Filtering
  • Content
    • Appropriate information about it
  • User profile
    • Relevant to the content

User Profile

Content

Give me only those I like

hybrid filtering
Hybrid Filtering
  • Combining both
    • Building on advantages
    • Overcoming limitations

User Profile

Content

what do you think amazon is using
What do you think Amazon is using?

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