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Hybrid Tag Recommendation in Collaborative Tagging Systems

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dc.contributor.author Lipczak, Marek
dc.date.accessioned 2012-04-20T18:00:14Z
dc.date.available 2012-04-20T18:00:14Z
dc.date.issued 2012-04-20
dc.identifier.uri http://hdl.handle.net/10222/14735
dc.description.abstract The simplicity and flexibility of tagging allows users to collaboratively create large, loosely structured repositories of Web resources. One of its main drawbacks is the need for manual formulation of tags for each posted resource. This task can be eased by a tag recommendation system, the objective of which is to propose a set of tags for a given resource, user pair. Tag recommendation is an interesting and well-defined practical problem. Its main features are constant interaction with users and availability of large amounts of tagged data. Given the opportunities (e.g., rich user feedback) and limitations (e.g., real-time response) of the tag recommendation setting, we defined six requirements for a practically useful tag recommendation system. We present a conceptual design and system architecture of a hybrid tag recommendation system, which meets all these requirements. The system utilizes the strengths of various tag sources (e.g., resource content and user profiles) and the relations between concepts captured in tag co-occurrence graphs mined from collaborative actions of users. The architecture of the proposed system is based on a text indexing engine, which allows the system to deal with large datasets in real time, while constantly adapting its models to newly added posts. The effectiveness and efficiency of the system was evaluated for six datasets representing a broad range of collaborative tagging systems. The experiments confirmed the high quality of results and practical usability of the system. In a comparative study the system outperformed a state-of-the-art algorithm based on tensor factorization for the most representative datasets applicable to both methods. The experiments on the characteristics of tagging data and the performance of the system allowed us to find answers to important research questions adapted from the general area of recommender systems. We confirmed the importance of infrequently used tags in the recommendation process and proposed solutions to overcome the cold start problem in tag recommendation. We demonstrated that a parameter tuning approach makes a hybrid tag recommendation system adaptable to various datasets. We also revealed the importance of the utilization of a feedback loop in the tag recommendation process. en_US
dc.language.iso en en_US
dc.subject tag recommendation en_US
dc.subject machine learning en_US
dc.subject recommendation en_US
dc.subject collaborative tagging en_US
dc.subject folksonomies en_US
dc.subject hybrid systems en_US
dc.title Hybrid Tag Recommendation in Collaborative Tagging Systems en_US
dc.date.defence 2012-03-15
dc.contributor.department Faculty of Computer Science en_US
dc.contributor.degree Doctor of Philosophy en_US
dc.contributor.external-examiner Denilson Barbosa en_US
dc.contributor.graduate-coordinator Qigang Gao en_US
dc.contributor.thesis-reader Michael Shepherd en_US
dc.contributor.thesis-reader Vlado Keselj en_US
dc.contributor.thesis-supervisor Evangelos Milios en_US
dc.contributor.ethics-approval Not Applicable en_US
dc.contributor.manuscripts Yes en_US
dc.contributor.copyright-release Yes en_US


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