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Fuzzy Information Filters for User Modeling in Collective Intelligence SystemsG. Castellano, C. Castiello, A.M. Fanelli, M. Lucarelli, C. MencarDept. of Informatics, University of Bari, Italy
Research outline↠ Purpose Define an abstract model for
representing users and resources ↠ Approach Fuzzy Information Filters (FIF).↠ Values Generality, adaptivity, handling imprecise
information, explainability↠ Impact personalized e-learning systems,
recommendation systems, community discovery, etc.
Collective Intelligence↠ Intelligence emerging from the interaction of
many individuals→ collaboration, competition, opinions, messaging, …
↠ Personalized experience in web applications↠ Information filtering
→ Based on data→ Based on a model
Information filtering↠ Fight information overload
→ the difficulty a person can have in making decisions caused by too much information. (Wikipedia)
↠ Deliver only relevant information→ User model
User model
↠ Preference→ What a user likes
↠ Competence→ What a user needs
↠ Knowledge→ What a user knows
Graduality & Granularity↠ Preferences & co. are always expressed to a
degree→ Ranking of objects according to prefs., needs, etc.
↠ Preferences & co. are often imprecise→ Refer to classes of objects instead of single individuals
Fuzzy Set Theory↠ Mathematical model
of granularity and graduality
↠ Extends classical set theory
Horror
Thriller
Drama
Fantasy
Comedy
Humor
Description-based filter↠ An object is represented as a collection of
metadata↠ Each metadata is defined by an attribute and a
fuzzy set of values↠ A description-based filter is defined by an
attribute and a fuzzy set of values
Matching
object
Description-based FIF
1. Given an object o={M1, M2, … Mn}
2. Given a Description-based FIF on attribute A and fuzzy set u
3. Find metadata M=(A,v) in o4. Match fuzzy set according to
possibility measureµ=maxxϵAmin{u(x),v(x),λ}
User model as FIF structure
OWA
(Simplified diagram: not all lines are drawn)
OWA
User likes cheap, lightweight, small cars which have a low-consumption engine and 4-5 doors
Filter learning↠ Filters can be designed by hand, or↠ they could be acquired from past observations
→ sequence of objects observed by a user↠ Theory of Possibility →
Principle of Minimum SpecificityI know John is a tall man (more than about 180cm) ⊢ Now I know John is within about
180-190 cmYou tell me John is not so tall (less than about 190cm)
Learning principles↠ Temporal Locality. If I observe an object, I will observe
the same object in the near future↠ Spatial Locality. If I observe an object, I will observe a
similar object↠ Relevance of knowledge. What I know has some
importance for learning↠ Relevance of observation. What I observe has some
importance for learning
Structural learning1. Given an observed object o and a filter f, a
matching degree d is calculated2. If d > threshold, then f is updated
a. Application of minimum specificity and learning principles3. Else a new filter is added in parallel to f
a. The new filter is a sequence of description based filters corresponding to metadata of o.
Conclusive remarks↠ Representation of complex user profiles↠ Filtering endowed with granularity and
graduality↠ Self-adaption to observed objects
Future research↠ Theory
→ Refinement of learning principles and structural learning→ Extended representation of user models→ Experiments with real-world data
↠ Application→ Integration within the Openness platform→ Service-oriented software system
