A probabilistic approach to personalized tag recommendation

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 fram...

Full description

Saved in:
Bibliographic Details
Main Authors: HU, Meiqun, LIM, Ee Peng, JIANG, Jing
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/619
https://ink.library.smu.edu.sg/context/sis_research/article/1618/viewcontent/auto_convert.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary: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.