A combined method for mitigating sparsity problem in tag recommendation

Djuana, Endang, Xu, Yue, Li, Yuefeng, & Josang, Audun (2014) A combined method for mitigating sparsity problem in tag recommendation. In Pasi, Gabriella & Pedrycz, Witold (Eds.) Proceedings of the 47th Annual Hawaii International Conference on System Sciences, Computer Society Press, Hilton Waikoloa, Big Island, Hawaii, US, pp. 906-915.

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Abstract

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items’ tags and existing tag ontology. We evaluated the approach on two public social bookmarking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

Impact and interest:

1 citations in Scopus
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ID Code: 62624
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Tag recommendation, Sparsity problem, Tags set expansion, Folksonomy, Ontology, Collaborative filtering
DOI: 10.1109/HICSS.2014.120
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2013 IEEE
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Deposited On: 19 Sep 2013 23:05
Last Modified: 23 Jun 2015 08:22

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