An efficient tagging data interpretation and representation scheme for item recommendation

Ifada, Noor & Nayak, Richi (2014) An efficient tagging data interpretation and representation scheme for item recommendation. In AusDM 2014 : The Twelfth Australasian Data Mining Conference, 27-28 November 2014, Queensland University of Technology, Gardens Point Campus, Brisbane, Australia.


A tag-based item recommendation method generates an ordered list of items, likely interesting to a particular user, using the users past tagging behaviour. However, the users tagging behaviour varies in different tagging systems. A potential problem in generating quality recommendation is how to build user profiles, that interprets user behaviour to be effectively used, in recommendation models. Generally, the recommendation methods are made to work with specific types of user profiles, and may not work well with different datasets. In this paper, we investigate several tagging data interpretation and representation schemes that can lead to building an effective user profile. We discuss the various benefits a scheme brings to a recommendation method by highlighting the representative features of user tagging behaviours on a specific dataset. Empirical analysis shows that each interpretation scheme forms a distinct data representation which eventually affects the recommendation result. Results on various datasets show that an interpretation scheme should be selected based on the dominant usage in the tagging data (i.e. either higher amount of tags or higher amount of items present). The usage represents the characteristic of user tagging behaviour in the system. The results also demonstrate how the scheme is able to address the cold-start user problem.

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ID Code: 79097
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: tagging, data interpretation, data representation, item recommendation, user profile, cold-start user
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the authors]
Deposited On: 01 Dec 2014 23:54
Last Modified: 20 Mar 2016 23:08

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