Search Personalization in Folksonomy by Exploiting Multiple and Temporal Aspects of User Profiles

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Social tagging data, also known as folksonomy, are a valuable indication for the user's understanding of a resource. The nature of folksonomy data in which a user annotates a resource with their opinions provides immense potential to contribute to search personalization. The challenge lies in extracting interests from the folksonomy data and building accurate user profiles while maintaining their characteristics. Furthermore, the current state-of-the-art technologies that utilize folksonomy for search personalization have not fully exploited both multiple and temporal aspects in user profiles. In this paper, we propose a search personalization framework that constructs a user profile network with identification of the multiple topics of the user and the temporal values of tags. Then, the user profile network is further explored through a link analysis technique for the network to score the tags by their importance. The performance of the proposed framework is evaluated against various state-of-the-art folksonomy-based personalization models and it consistently outperforms all of the compared models under the conditions of the best combination of ranking functions and link analysis techniques.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-08
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.7, pp.95610 - 95619

ISSN
2169-3536
DOI
10.1109/ACCESS.2019.2927026
URI
http://hdl.handle.net/10203/266065
Appears in Collection
IE-Journal Papers(저널논문)
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