Software design, implementation, application, and refinement of a Bayesian approach for the assessment of content and user qualities
Author
Türk, Melihcan
Advisor
Güvenir, Halil Altay
Date
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Show full item recordAbstract
The internet provides unlimited access to vast amounts of information. Technical
innovations and internet coverage allow more and more people to supply contents
for the web. As a result, there is a great deal of material which is either inaccurate
or out-of-date, making it increasingly difficult to find relevant and up-to-date content.
In order to solve this problem, recommender systems based on collaborative
filtering have been introduced. These systems cluster users based on their past
preferences, and suggest relevant contents according to user similarities. Trustbased
recommender systems consider the trust level of users in addition to their
past preferences, since some users may not be trustworthy in certain categories
even though they are trustworthy in others. Content quality levels are important
in order to present the most current and relevant contents to users. The study
presented here is based on a model which combines the concepts of content quality
and user trust. According to this model, the quality level of contents cannot
be properly determined without considering the quality levels of evaluators. The
model uses a Bayesian approach, which allows the simultaneous co-evaluation of
evaluators and contents. The Bayesian approach also allows the calculation of
the updated quality values over time. In this thesis, the model is further refined
and configurable software is implemented in order to assess the qualities of users
and contents on the web. Experiments were performed on a movie data set and
the results showed that the Bayesian co-evaluation approach performed more effectively
than a classical approach which does not consider user qualities. The
approach also succeeded in classifying users according to their expertise level.
Keywords
Information qualityWeb 2.0
Collaborative systems
Recommender systems
Collaborative filtering
Bayesian networks
Co-evaluation
Trust-based systems