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Frontiers of Electrical and Electronic Engineering

ISSN 2095-2732

ISSN 2095-2740(Online)

CN 10-1028/TM

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Front Elect Electr Eng    0, Vol. Issue () : 254-260    https://doi.org/10.1007/s11460-011-0177-4
RESEARCH ARTICLE
Using log mining to analyze user behavior on search engine
Ke XIE(), Huijia YU, Rongwei CEN
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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Abstract

Users’ behavior analysis has become one of the most important research topics, especially in terms of performance optimization, architecture analysis, and system maintenance, due to the rapid growth of search engine users. By adequately performing analysis on log data, researchers and Internet companies can get guidance to better search engines. In this paper, we perform our analysis based on approximately 750 million entries of search requests obtained from log of a real commercial search engine. Several aspects of users’ behavior are studied, including query length, ratio of query refining, recommendation access, and so on. Different information needs may lead to different behaviors, and we address this discussion in this paper. We firmly believe that these analyses would be helpful with respect of improving both effectiveness and efficiency of search engines.
Keywords user behavior analysis      search engine      web information retrieval     
Corresponding Author(s): XIE Ke,Email:oeddyo@gmail.com   
Issue Date: 05 June 2012
 Cite this article:   
Ke XIE,Huijia YU,Rongwei CEN. Using log mining to analyze user behavior on search engine[J]. Front Elect Electr Eng, 0, (): 254-260.
 URL:  
https://academic.hep.com.cn/fee/EN/10.1007/s11460-011-0177-4
https://academic.hep.com.cn/fee/EN/Y0/V/I/254
Fig.1  Interaction between user and search engine
data in logdescription
iduser’s ID
queryquery a user submitted
URLURL the user clicked
actionaction of the user, including click “next page”
timetime stamp of each action
Tab.1  Data included in log
Fig.2  Length distribution of search queries
length of queryratio
1 term44.37%
2 terms36.22%
3 terms12.56%
more than 3 terms6.85%
Tab.2  Query term number distribution of Chinese search engine users in 2006
type of queryratio of query refining
all56.1%
navigation query42.2%
sex-related query50.8%
Tab.3  Ratio of query refining over different query types
type of queryratio of query recommendation clicked
all11.9%
navigation query3.9%
sex-related query11.4%
Tab.4  Click ratio of query recommendation over different query types
number of queriesquestion like queriesratio
283 million8.38 million2.96%
Tab.5  Ratio of queries for asking questions
type of queryratio of result click
all56.0%
navigation query70.8%
sex-related query62.7%
Tab.6  Ratio of result click
Fig.3  Time interval distribution of the first click of user
Fig.4  Rank distribution of clicks in 2006
Fig.5  Rank distribution of the first/last click. (a) Rank distribution of the first click; (b) rank distribution of the last click
Fig.6  Click number distribution of the same query
number of queries in a sessionratio
166.46%
217.36%
3more than 37.33%8.85%
Tab.7  Distribution of query quantity in each session
different methodsratio
adding terms9.00%
deleting terms1.43%
totally change the queryothers82.37%6.30%
Tab.8  Queries modified methods distribution of Chinese search engine users within a session
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