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An Incremental Clustering Approach to Personalized Tag Recommendations

  • Yen-Hsien Lee
  • Tsai-Hsin ChuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11589)

Abstract

Volumes of user-generated contents have caused the problem of information overload and hindered Internet users from browsing and retrieving information. Social tagging that allows users to annotate resources with free preferred keywords to ease the access to their collecting resources. Though social tagging benefits users managing their resources, it always suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag because tags are freely and voluntarily assigned by users. Tag recommender systems, which follow some criteria to select from the tag space the most relevant tags to the user’s annotating resource, drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. This study takes personalized tag recommendation as an incremental clustering problem and proposes a Progressive Expansion-based Tag (PET) recommendation technique. The incremental clustering assumes each object appears in sequence and then is incrementally clustered into either an appropriate existing category or a created new category. The PET technique can classify each resource into multiple categories (i.e., tags) or label it as new. While a resource is labelled as new, it will recommend a set of tags that have been used by other users and are relevant to the target user’s practices. Finally, our empirical evaluation results suggest that the proposed PET technique outperforms the traditional popularity-based tag recommendation methods, while the performance rates achieved by both techniques are not satisfying.

Keywords

Tag recommender systems Personalized tag recommendation Incremental clustering Progressive tag expansion Social tagging 

Notes

Acknowledgement

This work was supported by Ministry of Science and Technology of the Republic of China under the grant MOST 106-2410-H-415-009.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Management Information SystemNational Chiayi UniversityChiayi CityTaiwan
  2. 2.Department of E-Learning Design and ManagementNational Chiayi UniversityChiayi CityTaiwan

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