Output details
11 - Computer Science and Informatics
University of Glasgow
Article title
Categorising social tags to improve folksonomy-based recommendations
Type
D - Journal article
Title of journal
Web Semantics: Science, Services and Agents on the World Wide Web
Article number
-
Volume number
9
Issue number
1
First page of article
1
ISSN of journal
1570-8268
Year of publication
2011
URL
-
Number of additional authors
2
Additional information
<17> This work is published in the top journal in the field of semantic web which has a 5-year impact factor 3.049. In this work we showed that semantic categorisation can be applied on real-world data sets and can be exploited for improving recommendations quality. We have used an ontology (YAGO) to categorise the noisy tags and developed a collaborative recommendation algorithm. In this work, we demonstrated that the ontology based categorization has high accuracy and can be used in real-life applications. The data set created for our evaluation work is used widely (as evident from the number of downloads and citations).
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
27
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
