Bookmark Category Web Page Classification Using Four Indexing and Clustering Approaches

Authors:
Chris Staff
Affiliations:
Department of Artificial Intelligence, University of Malta, Malta,
Venue:
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Year:
2008

Citing 3
Cited 0

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Abstract

Web browser bookmark files store records of web pages that the user would like to revisit. We use four methods to index and automatically classify documents referred to in 80 bookmark files, based on document title-only and full-text indexing and two clustering approaches. We evaluate the approaches by selecting a bookmark entry to classify from a bookmark file, re-creating a snapshot of the bookmark file to contain only entries created before the selected bookmark entry. The baseline algorithm is 39% accurate at rank 1 when the target category contains 7 entries. By fusing the recommendations of the 4 approaches, we reach 78.7% accuracy on average, recommending at most 3 categories.