Finding progression stages in time-evolving event sequences

WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web

Volumn , Issue , 2014, Pages 783-793

  Yang, Jaewon ^a,c   McAuley, Julian ^a   Leskovec, Jure ^a   LePendu, Paea ^b   Shah, Nigam ^b  

^a Stanford University   (United States)

^b STANFORD UNIVERSITY   (United States)

^c LINKEDIN CORPORATION   (United States)

Author keywords

Event sequences; Time series; User modeling

Indexed keywords

LARGE DATASET; TIME SERIES;

EVENT SEQUENCE; GENERATIVE MODEL; LARGE-SCALE DATASETS; MODEL-BASED METHOD; PRODUCT REVIEWS; SCALABLE ALGORITHMS; TEMPORAL EVOLUTION; USER MODELING;

REAL TIME SYSTEMS;

EID: 84907032880     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2566486.2568044     Document Type: Conference Paper

References (26)

1. Y. Ahn, J. Bagrow, and S. Lehmann. Link communities reveal multi-scale complexity in networks. Nature, 2010.
2. I. Batal, D. Fradkin, J. Harrison, F. Moerchen, and M. Hauskrecht. Mining recent temporal patterns for event detection in multivariate time series data. In KDD, 2012.
3. L. Bergroth, H. Hakonen, and T. Raita. A survey of longest common subsequence algorithms. In String Processing and Information Retrieval, 2000.
4. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Reserach, 2003.
5. E. Coviello, A. B. Chan, and G. R. G. Lanckriet. The variational hierarchical em algorithm for clustering hidden markov models. In NIPS, 2012.
6. C. Danescu-Niculescu-Mizil, R. West, D. Jurafsky, J. Leskovec, and C. Potts. No country for old members: user lifecycle and linguistic change in online communities. In WWW, 2013.
7. P. Felzenszwalb, D. Huttenlocher, and J. Kleinberg. Fast algorithms for large state space HMMs with applications to web usage analysis. In NIPS, 2003.
8. J. Kiernan and E. Terzi. Constructing comprehensive summaries of large event sequences. ACM Transaction on Knowledge Discovery from Data, 2009.
9. Y. Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010.
10. H. Kramer, Q. Nguyen, G. Curhan, and C. Hsu. Renal insufficiency in the absence of albuminuria and retinopathy among adults with type 2 diabetes mellitus. The Journal of the American Medical Association, 2003.
11. S. Laxman, V. Tankasali, and R. White. Stream prediction using a generative model based on frequent episodes in event sequences. In KDD, 2008.
12. N. Leeper, A. Bauer-Mehren, S. Iyer, P. LePendu, C. Olson, and N. Shah. Practice-based evidence: Profiling the safety of cilostazol by text-mining of clinical notes. PLoS ONE, 2013.
13. M. Liu and J. Qu. Mining high utility itemsets without candidate generation. In CIKM, 2012.
14. H. P. Lowe H., Ferris T. and W. S. STRIDE-an integrated standards-based translational research informatics platform. AMIA, 2009.
15. Y. Matsubara, Y. Sakurai, C. Faloutsos, T. Iwata, and M. Yoshikawa. Fast mining and forecasting of complex time-stamped events. In KDD, 2012.
16. J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In WWW, 2013.
17. D. Patnaik, S. Laxman, B. Chandramouli, and N. Ramakrishnan. Efficient episode mining of dynamic event streams. In ICDM, 2012.
18. J. Pei, H. Wang, J. Liu, K. Wang, J. Wang, and P. Yu. Discovering frequent closed partial orders from strings. IEEE Transactions on Knowledge and Data Engineering, 2006.
19. L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE, 1989.
20. S. Scott. Bayesian methods for hidden markov models. Journal of the American Statistical Association, 2002.
21. D. Shahaf, J. Yang, C. Suen, J. Jacobs, H. Wang, and J. Leskovec. Information cartography: creating zoomable, large-scale maps of information. In KDD, 2013.
22. P. Smyth. Clustering sequences with hidden markov models. In NIPS, 1997.
23. C. Suen, S. Huang, C. Eksombatchai, R. Sosic, and J. Leskovec. Nifty: a system for large scale information flow tracking and clustering. In WWW, 2013.
24. N. Tatti and J. Vreeken. The long and the short of it: summarising event sequences with serial episodes. In KDD, 2012.
25. B. West and J. Leskovec. Human wayfinding in information networks. In WWW, 2012.
26. C.-W. Wu, Y.-F. Lin, P. Yu, and V. Tseng. Mining high utility episodes in complex event sequences. In KDD, 2013.