Auto-scaled Bayesian browsing model in massive data

Author

Liyun Ru ; Anhui Wang ; Yingying Wu ; Shaoping Ma

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

fYear

2012

fDate

Oct. 30 2012-Nov. 1 2012

Firstpage

29

Lastpage

33

Abstract

In the field of information retrieval, the method of building a click model by mining click logs to improve the effect of the search engine has been widely studied. And Bayesian Browsing Model (BBM), for the calculation of the operability and the effectiveness of the result, is widely used. However, when applied in engineering, especially for the situation of large scale data, this model will not perform properly. This problem is described analytically and shown by numerical experiments in this paper. For this problem, an auto-scaled BBM method is proposed. Experiments show that the new method solves the problem of original BBM, and have a better performance in terms of NDCG.

Keywords

Bayes methods; Internet; data mining; information retrieval; search engines; NDCG; autoscaled BBM method; autoscaled Bayesian browsing model; click log mining; click model; information retrieval; large scale data; massive data processing; search engine; Bayes methods; Computational modeling; Data mining; Data models; Search engines; Vectors; Bayesian; Click logs; Click model;

fLanguage

English

Publisher

ieee

Conference_Titel

Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-1855-6

Type

conf

DOI

10.1109/CCIS.2012.6664361

Filename

6664361