Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems
One of the most noticeable innovation that emerged with the advent of the Web 2.0 and the focal point of this thesis are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords, so called tags. The tags are used for navigation, finding resources, and serendipitous browsing and thus provide an immediate benefit for the user. The conceptual structure underlying collaborative tagging systems is called folksonomy.
In this thesis a new data mining task – the mining of all frequent tri-concepts – is presented, together with an efficient algorithm for discovering such implicit shared conceptualizations.
Collaborative tagging systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource. We compare several recommendation algorithms on large-scale real-world datasets: an adaptation of user-based Collaborative Filtering, a graph-based recommender, and simpler methods based on tag co-occurences.
The social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind, has been developed by our research group. This thesis introduces BibSonomy as an exemplary collaborative tagging system and gives an overview of its architecture and some of its features.