NDLI: Real-time automatic tag recommendation

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Real-time automatic tag recommendation

Content Provider	ACM Digital Library
Author	Song, Yang Zhuang, Ziming Lee, Wang-Chien Zhao, Qiankun Giles, C. Lee Li, Huajing Li, Jia
Abstract	Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.
Starting Page	515
Ending Page	522
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390423
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graph partitioning Tagging system Mixture model
Content Type	Text
Resource Type	Article

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