1. Automatic cLasification d
a PArallelism
Data Mining based User Modeling Systems for Web Personalization applied to people with disabilities
J. Abascal, O. Arbelaitz, J. Muguerza, I. Perona
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

2. Contribution of Data Mining
Schedule
Introduction
Contribution of Data Mining
Profile generation
Exploitation
Summary
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

3. Objective in any context:
Introduction
Objective in any context:
To adapt web pages to the need of the users
Adaptation becomes especially critical when the users have special needs
Blind people: needs sounds (speech synthesis)‏
Low vision: large images and font sizes, bright colours …
Motor problems: simple links or buttons
Cognitive disabilities: simpler texts
We need to:
Build a model of the user that aggregates its main characteristics
Use this model to perform actions that make easier its information acquisition in the Web
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

4. How would we like to do this adaptation?
Introduction
This way the adaptive system can adapt the interaction to the concrete users
How would we like to do this adaptation?
Without the user doing an explicit demand
Automatically and dynamically adapting to the general characteristics of the user and the moment situation (mood, physical state, used device…)
Different kinds of features can be modeled to adjust the interface
User
Navigational behavior
Preferences
Physical sensory or cognitive restrictions
Context
Moment interest
Device
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

5. Adaptive systems composed of
Introduction
Adaptive systems composed of
Modeling component profiles and stereotypes to make assumptions about the characteristics of the user
Usually built by means of ontologies (concept hierarchies)that allow to store, manipulate and extract assumptions from data about the user, its context, tasks, etc.
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

6. Two options to acquire information:
General schema
Ontology
User
Profiles
Adapted
interface
User’s
information
Two options to acquire information:
Manually
Automatically
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

7. Directly designed by experts in the area. Rule based approach
Manual option
Directly designed by experts in the area.
Rule based approach
Rules generated based on the experts’ knowledge of the needs of different kinds of users
Drawbacks:
Profiles are artificially generated
Need of the expert each time the interface needs to be adapted to a new kind of user
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

8. Automatic option
When the user is a person with disabilities, data mining is a way to automatically process information about the uses of the person
Data mining for web personalization is based on statistical data obtained from real navigation data
When the characteristics of the user change, collected data allows the automatic change of the interaction schema
Learning from the own interaction allows maintaining a dynamic profile of the user, avoiding the application of all assumptions when the interest, characteristics or circumstances of the user change
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

9. Different approaches to build models
Automatic option
Different approaches to build models
Based on previous information from that user such as the logs of previous navigations (content-based approach)
Information about groups of users with similar characteristics (collaborative approach)
Our aim: to combine them to find a trade-off high specialization/computationally too expensive
Obtaining user information in automatic approaches
Two main sources:
Obtained in the client part: desktop applications…
Obtained in the server: browsing history…
Using some of them can violate the user’s privacy
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

10. Techniques: unsupervised learning techniques or clustering techniques
Automatic option
Techniques: unsupervised learning techniques or clustering techniques
Output: sets of users with similar characteristics or needs
Need of a distance metric
In a determined dimensional space: Manhattan, Euclidean distance, cosine similarity …
For sequences (click streams in a web, visited web pages, etc.)
Edit distance
Normalized Compression Distance…
The used clustering techniques (SAHN, Fixed-width or Leader algorithm, k-means) will depend on:
The nature of the data (vector like, sequence like)
The selected distances
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

11. Automatic option
To establish the profiles related to each cluster or group of similar users we propose:
Meta-learning techniques based for example in classification trees to profile the built clusters
Paradigms such as association rules or frequent episodes so that we can predict the most probable transitions between links for a kind of user
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

12. Automatic option Advantages:
The learners can automatically generate profiles for new kinds of users
Profiles are generated based in real browsing data (not artificial)
New profiles can appear if the users change (incremental learning, enrich the ontology)
Profiles based on the concepts in the ontology (different combinations)
Profiles with new concepts.
Machine learning used to validate new concepts
Need of an significance analysis
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

13. Exploitation .
A user navigating in the web needs to be matched with one of the previously generated profiles or a new one
Ontology
User
Profiles
Adapted
interface ??
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

14. Two main approaches to decide the profile of a user
Exploitation
Two main approaches to decide the profile of a user
Explicit: using some questionnaire (game??)
Disturbs the user with an explicit information demand making him/ her feel uncomfortable.
There is high probability of the user answering with not the whole true about his/her characteristics
Will not adapt to the conditions of a user in an concrete moment (tired or not, in a hurry…) just adapted to one of the existing profiles
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

15. Implicit: based on the browsing information of the user
Exploitation
Implicit: based on the browsing information of the user
This approach has the problem of the ti (cold start)
The user does not feel uncomfortable
There is no lying possibility
Using machine learning can adapt to the conditions of a user in an concrete moment (tired or not, in a hurry…)
Techniques: supervised classification based techniques
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

16. Data Mining techniques:
Summary
Goals:
Acquisition of user information to build profiles
Classification of new users on previously defined profiles or stereotypes or a new one
Data Mining techniques:
Unsupervised techniques combined with classification trees or similar
Supervised techniques
Data Mining contributions:
From the user point of view: comfortable
Incremental learning capacity. Adapting to new kinds of users of new situations
Enrichment of ontologies (hybrid solutions)
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores

17. Thanks for your attention!!!
Konputagailu Arkitektura eta Teknologia Saila
Departamento de Arquitectura y Tecnología de Computadores