Using time topic modeling for semantics-based dynamic research interest finding

Knowledge-Based Systems

Volumn 26, Issue , 2012, Pages 154-163

  Daud, Ali ^a  

^a INTERNATIONAL ISLAMIC UNIVERSITY   (Pakistan)

Author keywords

Dynamic research interests; Exchangeability of topics; Social networks; Time topic modeling; Unsupervised machine learning

Indexed keywords

ACTIVE AREA; EMPIRICAL RESULTS; EXCHANGEABILITY OF TOPICS; INTRINSIC STRUCTURES; MIXTURE DISTRIBUTIONS; MODELING APPROACH; REAL-WORLD APPLICATION; RESEARCH PAPERS; SOCIAL NETWORKS; SOCIAL TAGGING; TIME FACTORS; TIME STAMPS; TIME-PERIODS; TOPIC MODEL; UNSUPERVISED MACHINE LEARNING; WRITING STYLE;

MODELS; RESEARCH; SEMANTICS; SOCIAL NETWORKING (ONLINE);

REAL TIME SYSTEMS;

EID: 84155189116     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2011.07.015     Document Type: Article

References (26)

1. C. Andrieu, N.D. Freitas, A. Doucet, and M.I. Jordan An introduction to MCMC for machine learning Journal of Machine Learning 50 2003 5 43
2. L. Azzopardi, M. Girolami, K.V. Risjbergen, Investigating the Relationship between language model perplexity and IR precision-recall measures, in: Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, July 28-August 1, 2003.
3. K. Balog, T. Bogers, L. Azzopardi, M.D. Rijke, A.V.D. Bosch, Broad expertise retrieval in sparse data environments, in: Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, 2007, pp. 551-558. (Pubitemid 350165003)
4. D.M. Blei, A.Y. Ng, and M.I. Jordan Latent Dirichlet allocation Journal of Machine Learning Research 3 2003 993 1022
5. D.M. Blei, and J.D. Lafferty, Dynamic topic models, in: Proceedings of the International Conference on Machine Learning (ICML), 2006, pp. 113-120.
6. A. Daud, J. Li, L. Zhou, F. Muhammad, A generalized topic modeling approach for maven search, in: Proceedings of the International Asia-Pacific Web Conference and Web-Age Information Management (APWEB-WAIM), Suzhou, China, 2009.
7. A. Daud, J. Li, L. Zhou, and F. Muhammad Knowledge discovery through parametric directed probabilistic topic models. A survey Journal of Frontiers of Computer Science in China (FCS) 4 2 2010 280 301
8. A. Daud, J. Li, L. Zhu, and F. Muhammad Temporal expert finding through generalized time topic modeling Knowledge Based Systems (KBS) 23 6 2010 615 625
9. DBLP Bibliography Database. .
10. J. Diederich, J. Kindermann, E. Leopold, and G. Paass Authorship attribution with support vector machines Applied Intelligence 19 1 2003
11. C. Erten, P.J. Harding, S.G. Kobourov, K. Wampler, G. Yee, Exploring the Computing Literature using Temporal Graph Visualization, Technical Report, Department of Computer Science, University of Arizona, 2003.
12. A. Gray, P. Sallis, S. MacDonell, Softwareforensics: extending authorship analysis techniques to computer programs, in: Proceedings of the 3rd IAFL, Durham, NC, 1997.
13. T.L. Griffiths, M. Steyvers, Finding scientific topics, in: Proceedings of the National Academy of Sciences (NAS), USA, 2004, pp. 5228-5235. (Pubitemid 38469131)
14. A. McCallum, Multi-label text classification with a mixture model trained by EM, in: Proceedings of the AAAI'99 Workshop on Text Learning, 1999.
15. D. Mimno, A. McCallum, Expertise modeling for matching papers with reviewers, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, pp. 500-509. (Pubitemid 350229235)
16. P. Mutschke Mining networks and central entities in digital libraries: a graph theoretic approach applied to co-author networks Intelligent Data Analysis 2003 155 166
17. M.E.J. Newman Scientific collaboration networks: I. Network construction and fundamental results Physical Review E 64 2001 016131
18. M. Rosen-Zvi, C. Chemudugunta, T. Griffiths, P. Smyth, and M. Steyvers Learning author-topic models from text corpora ACM Transactions on Information Systems 2009 1 38
19. M. Steyvers, P. Smyth, T. Griffiths, Probabilistic author-topic models for information discovery, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, 2004.
20. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, Z. Su, ArnetMiner: extraction and mining of academic social networks, in: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
21. Y.W. Teh, M.I. Jordan, M.J. Beal, D.M. Blei, Hierarhical Dirichlet Processes. Technical Report 653, Department of Statistics, UC Berkeley, 2004.
22. X. Wang, A. McCallum, Topics over Time: A Non-Markov continuous-time model of topical trends, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 424-433. (Pubitemid 44535539)
23. S. White, P. Smyth, Algorithms for estimating relative importance in networks, in: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 266-275.
24. C. Wang, M.D. Blei, D. Heckerman, Continuous time dynamic topic models, in: Proceedings of the Uncertainty in Artificial Intelligence (UAI), Helsinki, Finland, July 9-12, 2008.
25. J. Zhang, J. Tang, J. Li, Expert finding in a social network, in: Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA), 2007.
26. H. Dong, and F.K. Hussain Semantic service matchmaking for digital health ecosystems Knowledge Based Systems (KBS) 24 2011 761 774