Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

8-2010

Abstract

In this work, we study the task of personalized tag recommendation in social tagging systems. To reach out to tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for incorporating translations by similar users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such similarity measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with personomy translation methods based on the query user solely and collaborative filtering. Our experimental results show that our neighbor based translation methods outperform these baseline methods significantly. Moreover, we show that the translations borrowed from neighbors indeed help ranking relevant tags higher than that based solely on the query user.

Keywords

Social tagging, Recommendation methods, Neighbor-based translation method

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

SocialCom 2010: Proceedings: The second IEEE International Conference on Social Computing: Minneapolis, Minnesota, August 20-22, 2010

First Page

33

Last Page

40

ISBN

9780769542119

Identifier

10.1109/SocialCom.2010.15

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.ieeecomputersociety.org/10.1109/SocialCom.2010.15

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