Time-sensitive query auto-completion

SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2012, Pages 601-610

  Shokouhi, Milad ^a   Radinsky, Kira ^b  

^a MICROSOFT RESEARCH   (United Kingdom)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

query auto completion; temporal ranking; time series

Indexed keywords

HIGH QUALITY; KEY FEATURE; QUERY AUTO-COMPLETION; TEMPORAL DYNAMICS; TEMPORAL RANKING; TEMPORAL TRENDS;

INFORMATION RETRIEVAL;

SEARCH ENGINES;

EID: 84866607494     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2348283.2348364     Document Type: Conference Paper

References (34)

1. E. Alfonseca, M. Ciaramita, and K. Hall. Gazpacho and summer rash: lexical relationships from temporal patterns of web search queries. In Proc. Conf. Empirical Methods in NLP, pages 1046-1055, Singapore, 2009.
2. Z. Bar-Yossef and N. Kraus. Context-sensitive query auto-completion. In Proc. WWW, pages 107-116, Hyderabad, India, 2011.
3. R. Baraglia, F. M. Nardini, C. Castillo, R. Perego, D. Donato, and F. Silvestri. The effects of time on query flow graph-based models for query suggestion. In Proc. RIAO, pages 182-189, Paris, France, 2010.
4. S. M. Beitzel, E. C. Jensen, A. Chowdhury, D. Grossman, and O. Frieder. Hourly analysis of a very large topically categorized web query log. In Proc. SIGIR, pages 321-328, Sheffield, United Kingdom, 2004.
5. K. Berberich, S. Bedathur, O. Alonso, and G. Weikum. A language modeling approach for temporal information needs. In Proc. ECIR, pages 13-25, Milton Keynes, UK, 2010.
6. S. Bickel, P. Haider, and T. Scheffer. Learning to complete sentences. In Proc. ECML, pages 497-504. 2005.
7. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, Mar. 2003.
8. S. Chaudhuri and R. Kaushik. Extending autocompletion to tolerate errors. In Proc. SIGMOD, pages 707-718, Providence, Rhode Island, USA, 2009.
9. S. Chien and N. Immorlica. Semantic similarity between search engine queries using temporal correlation. In Proc. WWW, pages 2-11, Chiba, Japan, 2005.
10. H. Cho and H. Varian. Predicting the present with google trends. Technical report, Google Inc, April 2009.
11. N. Dai and B. D. Davison. Freshness matters: in flowers, food, and web authority. In Proc. SIGIR, pages 114-121, Geneva, Switzerland, 2010.
12. N. Dai, M. Shokouhi, and B. D. Davison. Learning to rank for freshness and relevance. In Proc. SIGIR, pages 95-104, Beijing, China, 2011.
13. J. J. Darragh, I. H. Witten, and M. L. James. The reactive keyboard: A predictive typing aid. Computer, 23:41-49, November 1990.
14. A. Dong, R. Zhang, P. Kolari, J. Bai, F. Diaz, Y. Chang, Z. Zheng, and H. Zha. Time is of the essence: improving recency ranking using twitter data. In Proc. WWW, pages 331-340, Raleigh, North Carolina, USA, 2010.
15. M. Efron. Linear time series models for term weighting in information retrieval. J. Am. Soc. Inf. Sci. Technol., 61, July 2010.
16. J. L. Elsas and S. T. Dumais. Leveraging temporal dynamics of document content in relevance ranking. In Proc. WSDM, 2010.
17. J. Fan, H. Wu, G. Li, and L. Zhou. Suggesting topic-based query terms as you type. In Proc. APWEB, pages 61-67, Washington, DC, 2010.
18. K. Grabski and T. Scheffer. Sentence completion. In Proc. SIGIR, pages 433-439, Sheffield, United Kingdom, 2004.
19. C. C. Holt. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1):5-10, 2004.
20. S. Ji, G. Li, C. Li, and J. Feng. Efficient interactive fuzzy keyword search. In Proc. WWW, pages 371-380, Madrid, Spain, 2009.
21. R. Jones and F. Diaz. Temporal profiles of queries. ACM Trans. Inf. Syst., 25, July 2007.
22. N. Kanhabua and K. Nørvåg. Determining time of queries for re-ranking search results. In Proc. ECDL, 2010.
23. A. Kulkarni, J. Teevan, K. M. Svore, and S. T. Dumais. Understanding temporal query dynamics. In Proc. WSDM, pages 167-176, Hong Kong, China, 2011.
24. X. Li and W. B. Croft. Time-based language models. In cikm, pages 469-475, 2003.
25. N. Liu, J. Yan, S. Yan, W. Fan, and Z. Chen. Web query prediction by unifying model. In Proc. IEEE Int. Conf. Data Mining Workshops, pages 436-441, Washington, DC, USA, 2008.
26. D. Metzler, R. Jones, F. Peng, and R. Zhang. Improving search relevance for implicitly temporal queries. In Proc. SIGIR, pages 700-701, Boston, MA, USA, 2009.
27. A. Nandi and H. V. Jagadish. Effective phrase prediction. In Proc. VLDB, pages 219-230, Vienna, Austria, 2007.
28. P. L. R. Byrd and J. Nocedal. SIAM Journal on Scientific and Statistical Computing, 16(5):1190-1208, 1995.
29. K. Radinsky, K. M. Svore, J. Teevan, S. T. Dumais, A. Bocharov, and E. Horvitz. Learning and predicting behavioral dynamics on the web. In Proc. of WWW, pages to-appear, 2011.
30. Y. Shimshoni, N. Efron, and Y. Matias. On the predictability of search trends. Technical report, Google Inc, August 2009.
31. M. Shokouhi. Detecting seasonal queries by time-series analysis. In Proc. SIGIR, pages 1171-1172, Beijing, China, 2011.
32. M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos. Identifying similarities, periodicities and bursts for online search queries. pages 131-142, Paris, France, 2004.
33. R. W. White and G. Marchionini. Examining the effectiveness of real-time query expansion. Inf. Process. Manage., 43:685-704, May 2007.
34. R. Zhang, Y. Chang, Z. Zheng, D. Metzler, and J.-y. Nie. Search result re-ranking by feedback control adjustment for time-sensitive query. In Proc. NAACL-Short, pages 165-168, Boulder, Colorado, 2009. Association for Computational Linguistics.