Predicting interesting things in text

COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers

Volumn , Issue , 2014, Pages 1477-1488

  Gamon, Michael ^a   Mukherjee, Arjun ^b   Pantel, Patrick ^a  

^a MICROSOFT   (United States)

^b University of Houston ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; COMPUTATIONAL LINGUISTICS; LINGUISTICS; SEMANTICS; WEB BROWSERS;

DISCRIMINATIVE LEARNING; GRAPHICAL MODEL; LATENT SEMANTICS; LEVEL OF INTEREST; NATURALLY OCCURRING; PROBABILISTIC INFERENCE; TEXTUAL CONTEXTS; USER BROWSING BEHAVIORS;

FORECASTING;

EID: 84959891993     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Agichtein, E., Brill, E., and Dumais, S. 2006. Improving web search ranking by incorporating user behavior information. In Proceedings of SIGIR. pp. 19-26.
2. Bandari, R., Asur, S., and Huberman, B. A. 2012. The Pulse of News in Social Media: Forecasting Popularity. In Proceedings of ICWSM.
3. Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
4. Broder, A., Fontoura, M., Josifovski, V., and Riedel, L. 2007. A semantic approach to contextual advertising. In Proceedings of SIGIR. pp. 559-566.
5. Bishop, C.M. 2006. Pattern Recognition and Machine Learning. Springer.
6. Chatterjee, P., Hoffman, D. L., and Novak, T. P. 2003. Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22(4), 520-541.
7. Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M. and Ghosh, R. 2013. Leveraging Multi-Domain Prior Knowledge in Topic Models. In Proceedings of IJCAI. pp. 2071-2077.
8. Claypool, M., Le, P., Wased, M., & Brown, D. 2001a. Implicit interest indicators. In Proceedings of the 6th international conference on Intelligent user interfaces (pp. 33-40). ACM.
9. Craswell, N., Zoeter, O., Taylor, M., and Ramsey, B. 2008. An experimental comparison of click position-bias models. In Proceedings of WSDM. pp. 87-94.
10. Erosheva, E., Fienberg, S., and Lafferty, J. 2004. Mixed membership models of scientific publications. In Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1). pp. 5220-5227.
11. Friedman, J. H. 1999. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 1999.
12. Gamon, M., Yano, T., Song, X., Apacible, J. and Pantel, P. 2013. Identifying Salient Entities in Web Pages. In Proceedings CIKM. pp. 2375-2380.
13. Gao, J., Toutanova, K., and Yih, W. T. 2011. Clickthrough-based latent semantic models for web search. In Proceedings of SIGIR. pp. 675-684.
14. Graepel, T., Candela, J.Q., Borchert, T., and Herbrich, R. 2010. Web-scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. In Proceedings of ICML. pp. 13-20.
15. Griffiths, T.L and Steyvers, M. 2004. Finding Scientific Topics. Proceedings of the National Academy of Science, 101, suppl 1, 5228-5235.
16. Guo, F., Liu, C., Kannan, A., Minka, T., Taylor, M., Wang, Y.-M, and Faloutsos, C. 2009a. Click chain model in web search. In Proceedings of WWW. pp. 11-20.
17. Guo, J., Xu, G., Cheng, X., and Li, H. 2009b. Named entity recognition in query. In Proceedings of SIGIR. pp. 267-274.
18. Joachims, T. 2002. Optimizing search engines using clickthrough data. In Proceedings of KDD. pp. 133-142.
19. Joachims, T., Granka, L., Pan, B., Hembrooke, H. and Gay, G. 2005. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of SIGIR. pp. 154-161.
20. Lerman, K., and Hogg, T. 2010. Using a model of social dynamics to predict popularity of news. In Proceedings of WWW. pp. 621-630.
21. Lin, T., Pantel, P., Gamon, M., Kannan, A., and Fuxman, A. 2012. Active objects: Actions for entity-centric search. In Proceedings of WWW. pp. 589-598.
22. Manning, C. D., Raghavan, P., and Schutze, H. 2008. Introduction to Information Retrieval. Cambridge University Press.
23. Mueller, F., & Lockerd, A. 2001. Cheese: Tracking mouse movement activity on websites, a tool for user modeling. In CHI'01 extended abstracts on Human factors in computing systems (pp. 279-280). ACM.
24. Paranjpe, D. 2009. Learning document aboutness from implicit user feedback and document structure. In Proceedings of CIKM. pp. 365-374.
25. Shen, S., Hu, B., Chen, W., and Yang, Q. 2012. Personalized click model through collaborative filtering. In Proceedings of WSDM. pp. 323-333.
26. Szabo, G., and Huberman, B. A. 2010. Predicting the popularity of online content. Com-munications of the ACM, 53(8), 80-88.
27. Varela, F. J., Thompson, E. T., & Rosch, E. 1991. The embodied mind: Cognitive science and human experience. The MIT Press.
28. Yano, T., Cohen, W. W., & Smith, N. A. 2009. Predicting response to political blog posts with topic models. In Proceedings of NAACL. pp. 477-485.