Semantic hashing using tags and topic modeling

SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2013, Pages 213-222

  Wang, Qifan ^a   Zhang, Dan ^b   Si, Luo ^a  

^a Purdue University   (United States)

^b FACEBOOK   (United States)

Author keywords

Hashing; Tags; Topic Modeling

Indexed keywords

DOCUMENT RELATIONSHIPS; HASHING; ITERATIVE COORDINATE DESCENTS; PROBABILISTIC TOPIC MODELS; SEMANTIC RELATIONSHIPS; SIMILARITY INFORMATIONS; TAGS; TOPIC MODELING;

BINARY CODES; DIGITAL STORAGE; INFORMATION RETRIEVAL; ITERATIVE METHODS; RESEARCH;

SEMANTICS;

EID: 84883066652     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2484028.2484037     Document Type: Conference Paper

References (40)

1. A. Ahmed, M. Aly, J. Gonzalez, S. Narayanamurthy, and A. J. Smola. Scalable inference in latent variable models. In WSDM, pages 123-132, 2012.
2. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, pages 459-468, 2006.
3. D. Blei and J. Lafferty. Topic models. Text Mining: Theory and Applications, 2009.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
5. T. Cormen, C. Leiserson, R. Rivest, and C. Stein. Introduction to algorithms. The MIT press, 2001.
6. J. S. Culpepper and A. Moffat. Efficient set intersection for inverted indexing. ACM Trans. Inf. Syst., 29(1):1, 2010.
7. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Symposium on Computational Geometry, pages 253-262, 2004.
8. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman. Indexing by latent semantic analysis. JASIS, 41(6):391-407, 1990.
9. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, pages 518-529, 1999.
10. G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
11. G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
12. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, pages 50-57, 1999.
13. T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In SIGIR, pages 259-266, 2003.
14. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, pages 263-272, 2008.
15. W. Kong and W.-J. Li. Isotropic hashing. In NIPS, pages 1655-1663. 2012.
16. B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In ICCV, pages 2130-2137, 2009.
17. K. Lang. Newsweeder: Learning to filter netnews. In ICML, pages 331-339, 1995.
18. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5:361-397, 2004.
19. R.-S. Lin, D. A. Ross, and J. Yagnik. Spec hashing: Similarity preserving algorithm for entropy-based coding. In CVPR, pages 848-854, 2010.
20. W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang. Supervised hashing with kernels. In CVPR, pages 2074-2081, 2012.
21. W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In ICML, pages 1-8, 2011.
22. R. Salakhutdinov and G. E. Hinton. Semantic hashing. Int. J. Approx. Reasoning, 50(7):969-978, 2009.
23. G. Salton. Developments in automatic text retrieval. Science, 253(5023):974-980, August 1991.
24. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Inf. Process. Manage., 24(5):513-523, 1988.
25. L. Si and R. Jin. Flexible mixture model for collaborative filtering. In ICML, pages 704-711, 2003.
26. F. Silvestri and R. Venturini. Vsencoding: efficient coding and fast decoding of integer lists via dynamic programming. In CIKM, pages 1219-1228, 2010.
27. B. Stein. Principles of hash-based text retrieval. In SIGIR, pages 527-534, 2007.
28. V. Vapnik. The nature of statistical learning theory. Springer Verlag, 2000.
29. C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In KDD, pages 448-456, 2011.
30. J. Wang, S. Kumar, and S.-F. Chang. Sequential projection learning for hashing with compact codes. In ICML, pages 1127-1134, 2010.
31. J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2393-2406, 2012.
32. Q. Wang, L. Si, and D. Zhang. A discriminative data-dependent mixture-model approach for multiple instance learning in image classification. In ECCV (4), pages 660-673, 2012.
33. R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In VLDB, pages 194-205, 1998.
34. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, pages 1753-1760, 2008.
35. H. Xia, P. Wu, S. C. H. Hoi, and R. Jin. Boosting multi-kernel locality-sensitive hashing for scalable image retrieval. In SIGIR, pages 55-64, 2012.
36. X. Yi and J. Allan. Evaluating topic models for information retrieval. In CIKM, pages 1431-1432, 2008.
37. L. Zelnik-Manor and P. Perona. Self-tuning spectral clustering. In NIPS, 2004.
38. D. Zhang, F. Wang, and L. Si. Composite hashing with multiple information sources. In SIGIR, pages 225-234, 2011.
39. D. Zhang, J. Wang, D. Cai, and J. Lu. Self-taught hashing for fast similarity search. In SIGIR, pages 18-25, 2010.
40. J. Zobel and A. Moffat. Inverted files for text search engines. ACM Comput. Surv., 38(2), 2006.