Real-time automatic tag recommendation

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.

[1]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Chris H. Q. Ding,et al.  Bipartite graph partitioning and data clustering , 2001, CIKM '01.

[3]  Hongyuan Zha,et al.  Computational Statistics Data Analysis , 2021 .

[4]  Bernardo A. Huberman,et al.  Usage patterns of collaborative tagging systems , 2006, J. Inf. Sci..

[5]  Petros Drineas,et al.  Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition , 2006, SIAM J. Comput..

[6]  Edward A. Fox,et al.  SimFusion: measuring similarity using unified relationship matrix , 2005, SIGIR '05.

[7]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[8]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[9]  Tie-Yan Liu,et al.  Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering , 2005, KDD '05.

[10]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[11]  Siegfried Handschuh,et al.  P-TAG: large scale automatic generation of personalized annotation tags for the web , 2007, WWW '07.

[12]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[13]  Ricardo A. Baeza-Yates,et al.  Query Recommendation Using Query Logs in Search Engines , 2004, EDBT Workshops.

[14]  Chris H. Q. Ding,et al.  A min-max cut algorithm for graph partitioning and data clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[15]  Yang Song,et al.  Social Bookmarking for Scholarly Digital Libraries , 2007, IEEE Internet Computing.

[16]  Grigory Begelman,et al.  Automated Tag Clustering: Improving search and exploration in the tag space , 2006 .