elektronická verzia publikácie - FIIT STU - Slovenská technická ...

User Modeling for Personalized Web-Based Systems 235
The actual classification function takes the new user instance (which should be classified)
and the k nearest neighbors. The value of the degree of knowledge and error proneness of the
new student (s q ) is computed as a distance weighted mean value of the degree of knowledge
and error proneness of the k students that belong to the same stereotype with the new student
(s 1 ,s 2 ,...,s k ). So the knowledge level for each concept is computed as:
KnowledgeLevel(Concept x ,s q )=
∑ k
i=1 w iKnowledgeLevel(Concept x ,s i )
∑ k
i=1 w i
(8.1)
where w i is the weight of the contribution of each student and is calculated as an inverse
square of its distance from s q :
w i =
8.5.4 Acquiring Information from External Sources
1
Δ(s q ,s i ) 2 (8.2)
In [47] authors propose the idea of a cold-start problem solving by providing some initial
knowledge about users and their domains of interest from shared ontologies. It should
thus be possible to bootstrap the initial learning phase of a recommender system with such
knowledge. They created the Quickstep recommender (Figure 8-7) which implements the
idea in the domain of scientific publications.
World Wide
Web
Users
Profiles
Classifier
Recommender
Classified papers
Figure 8-7. The Quickstep recommender system according to [47].
The ontology used to bootstrap the recommender was developed by Southampton’s AKT
team (Advanced Knowledge Technologies). It models people, projects, papers, events and
research interests, was populated with information extracted automatically from a departmental
personnel database and publication database. The ontology consists of around 80
classes, 40 slots, over 13000 instances [46].
When new user arrives, the recommender system retrieves an initial set of her publications
from the ontology. These publications are then correlated with the classified paper
database and a set of historical interests are compiled for that user. These historical interests
form the basis of an initial profile, overcoming the new system cold-start problem. Moreover,
system is trying to find out communities of practice for the current user (i.e., group of people
who share some common interest in a particular practice – a social network) in the ontology