Session Segmentation Method Based on Naïve Bayes Model

Abstract:

Article Preview

Session segmentation can not only contribute a lot to the further and deeper analysis of user’s search behavior but also act as the foundation of other retrieval process researches based on users’ complicated search behaviors. This paper proposes a session boundary discrimination model utilizing time interval and query likelihood on the basis of Naive Bayes Model. Compared with previous study, the model proposed in this paper shows a prominent improvement through experiment in three aspects, which is: recall ratio, precision ratio and value F. Owing to its advantage in session boundary discrimination, the application of the model can serve as a tool in fields like personalized information retrieval, query suggestion, search activity analysis and other fields which is related to search results improvement.

Info:

Periodical:

Edited by:

Wei Deng and Qi Luo

Pages:

576-582

DOI:

10.4028/www.scientific.net/AEF.6-7.576

Citation:

P. Li et al., "Session Segmentation Method Based on Naïve Bayes Model", Advanced Engineering Forum, Vols. 6-7, pp. 576-582, 2012

Online since:

September 2012

Export:

[1] Craig Silverstein, Monika Henzinger, Hannes Marais, et al. Analysis of a very large Web search engine query log [J]. In SIGIR Forum, fall 1998, 33(1): 6- 12.

DOI: 10.1145/331403.331405

[2] Daqing He, Ayse Goker. Detecting session boundaries from Web user logs[C]. Proceedings of the 22nd annual colloquium on information, 2000. pp: 57-66.

[3] H. Cenk Ozmutlu, Fatih cavdur. Application of automatic topic identification on excites web search engine data logs [J]. Information Processing and Management: an International Journal, 2005, 41(5): 1243-1262.

DOI: 10.1016/j.ipm.2004.04.018

[4] Seda Ozmutlu, Fatih Cavdur. Neural network applications for automatic new topic identification [J]. Online Information Review, 2005, 29(1): 34-53.

DOI: 10.1108/14684520510583936

[5] Seda Ozmutlu, H. Cenk Ozmutlu, Amanda Spink. Automatic New Topic Identification in Search Engine Transaction Logs using Multiple Linear Regression[C]. Proceedings of the 41st Hawaii International Conference on System Sciences. 2008. pp: 140-140.

DOI: 10.1109/hicss.2008.70

[6] Seda Ozmutlu, Huseyin C. Ozmutlu, Buket Buyuk. Using Monte-Carlo Simulation for Automatic New Topic Identification of Search Engine Transaction Logs[C]. Proceedings of the 2007 Winter Simulation Conference. 2007. pp: 2306-2314.

DOI: 10.1109/wsc.2007.4419869

[7] Huijia Yu, Yiqun Liu, Min Zhang, Liyun Ru, Shaoping Ma. Research in Search Engine User Behavior Based on Log Analysis [J]. Journal of Chinese Information Processing, Vol. 2007, 21(1): 109-114.

[8] ZHANG Lei, LI Yanan, WANG Bin, LI Peng, JIANG Zaifan. Session Segmentation Based on Query Logs of Web Search [J]. Journal of Chinese Information Processing, 2009, 23(2): 54-61.

[9] Xiangji Huang, Fuchun Peng, Aijun An, DaleSchuurmans. Dynamic Web Log Session Identificationwith Statistical Language Models [J]. Journal of the American Society for Information Science and Technology, 55 (14): 129021303.

DOI: 10.1002/asi.20084

[10] FANG Qi, LIU Yiqun, ZHANG Min, RUN Liyun, MA Shaoping Swarm Intelligence Based Topic Identification for Sessions in Web Access Log [J]. Journal of Chinese Information Processing. Vol. 2011, 25(1): 35-40.

In order to see related information, you need to Login.