Personalization in tag ontology learning for recommendation making

Djuana, Endang, Xu, Yue, Li, Yuefeng, & Cox, Clive (2012) Personalization in tag ontology learning for recommendation making. In Taniar, David, Pardede, Eric, Rahayu, Wenny, & Khalil, Ismail (Eds.) Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services, ACM Press (Association for Computing Machinery), Bali, Indonesia, pp. 368-377.

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Abstract

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

Impact and interest:

1 citations in Scopus
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ID Code: 56042
Item Type: Conference Paper
Refereed: Yes
Additional Information: All accepted iiWAS2012 papers will be published by ACM International Conference Proceedings Series (ISBN: 978-1-4503-1306-3)
DOI: 10.1145/2428736.2428804
ISBN: 9781450313063
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)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Organisation of Information and Knowledge Resources (080707)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2012 Association for Computing Machinery
Deposited On: 02 Jan 2013 07:06
Last Modified: 03 Aug 2014 20:49

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