User Modelling seminar
Jonathan Foss
Dana Al-Qudah
What is needed for UM?
• User Model
– Personal information (e.g. age?, culture?)?
– Location?
– Knowledge?
– Browsing History?
• Information needs to be collected somehow
Populating a UM
• Explicit – through user intervention
• Implicit – through agents that passively
monitor user activities
Build a User Model in Protégé


Build a user model based on the features you
have studied in the module in Protégé.
It should represent at least user knowledge,
cognitive properties, goals and plans, moods
and emotions, preferences.
Representing UMs
• No fixed standards for storing UM
• Can represent observations
– The user has visited the page ‘Coventry’ 5 times…
• Can also represent contain inferences
– e.g. …therefore the user probably has an interest
in Coventry
UMs within Education
• UMs could also be used within educational
websites
– Remember what the user has learnt
• e.g. don’t attempt to teach trigonometry before they’ve
understood Pythagoras
– People have different learning styles
• e.g. some people prefer to watch video lectures –
others prefer books…
Learning Styles
• Felder Solomon: ILS
• Check your LS at:
http://www.engr.ncsu.edu/learningstyles/ilsweb.
html (read:
http://www4.ncsu.edu/unity/lockers/users/f/feld
er/public/ILSpage.html
• Where do you think you are on Kolb’s scale?
• What about field dependent/independent,
impulsive/reflective; introvert/extrovert;
high/low fear of failure; etc. ?
How Social Networks collect
information…
• Explicit
– User clicks the Like Button
– User clicks the Google +1 button
• Implicit
– Behaviour within the social network
– Maybe just visiting a web page?
How does it know who my friends are?
Facebook must know I’ve visited the
Warwick Page
Facebook Like Button
• “If you’re logged in to Facebook and visit a website with the Like
button or another social plugin, your browser sends us
information about your visit. It’s important to note that Facebook
isn't retrieving this information. Since the Like button is a little
piece of Facebook embedded on another website, the browser is
sending information about the request to load Facebook content on
that page.
• We record some of this information for a limited amount of
time to help show you a personalized experience on that site and to
improve our products. For example, when you go to a website with a
Like button, we need to know who you are in order to show you
what your Facebook friends have liked on that site. The data we
receive includes your user ID, the website you're visiting, the date and
time and other browser-related information.”
– from http://www.facebook.com/help/186325668085084
User Models for Web Personalisation
The Amazon website is
embedded in an iframe – the
iframe has referrers from
Chortle
The iframe can read my
Amazon cookie - Amazon
therefore knows I’m on
Chortle, and could use this
information to recommend
Comedy to me next time
Exercise
• Have a look at some of the websites you’ve
been to recently
• What type of user modelling have you
experienced on them?
– Explicit/implicit; static/dynamic/long-term
– How did they know these things? (info collection?)
• What websites do they communicate with?
– Hint: FireBug or Chrome’s ‘Inspect Element’ have
tools to help you…
Feedback
What did you find?
Keyword profiles
• Bag of words
– Vector representation doesn’t consider the
ordering of words in a document
– John is quicker than Mary and Mary is quicker
than John have the same vectors
Keyword Profiles

Compare
“The University of Warwick is not in Warwick!”
 “Coventry has two Universities, Warwick and Coventry”
 “Warwick is near Leamington”


Tokenize and Remove Punctuation
0
1
2
3
4
5
6
7
The
University
of
Warwick
is
not
in
Warwick
Coventry
has
two
Universities
Warwick
and
Coventry
Warwick
is
near
Leamington
Keyword Profiles

Tokenize and Remove Punctuation
0
1
2
3
4
5
6
7
The
University
of
Warwick
is
not
in
Warwick
Coventry
has
two
Universities
Warwick
and
Coventry
Warwick
is
near
Leamington

Stem words and remove Stop Words
0
1
2
Universit
Warwick
Warwick
Coventry
Universit
Warwick
Warwick
Leamington
3
Coventry
Example Rule-based model

Pseudo-algorithm:
 Initialise
number_of_clicks;
 learning_style = unknown;
 If number_of_clicks on video > 3 learning_style = visual
 If number of _clicks on text > 3 learning style = textual

User behaviour:
 Clicks
2 times on video, 2 times on text, then again 2
times on video, 2 times on text, etc.
 Discuss what effect the algorithm above has on the
expected learning style of the user; discuss issues with
this approach;
 Propose corrections to the algorithm to alleviate the
issues.