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Research of Data Mining Based on E-Commerce
                                                                                                                             Liu Xiao-liang
                                Li Yong-hong,                                                          Dept. of Business Hebei University of Economics and
                                                                                                                                   Business
     Dept. of Account Hebei University of Economics and
                                                                                                                          Shijiazhuang, Hebei
                                       Business
                                                                                                                          lx1130225@163.com
                           Shijiazhuang, Hebei
                      liyonghong 1966@126.com


Abs1l'llcf-this paper describes the Web data mining teehnology,
Web data mining process, data sources and uses, designs a
personalized      recommendation               system     based       on   Web     use
mining for e-commerce, and discusses in detail the function of
each module of the system and implementation techniques.
Through the mining of the historical log data of Web server,                                          l'Cb mining              Web usage   mining

the system can access to the user browsing patterns to provide
personalized service for users.


     KI!)'H'01'fIs-IYeIJ tiotfl     lIIining; e-ctJlIII1II!1'Ce;   recollllllt!lllltllll
SJ'SIelll
                                                                                                                       Figure I.   Web mining classification
                               I.      INTRODUCTION

    With the increasing popularity of Internet and the rapid                                          J)     Web content mining
development of e-commerce, internet-based business Web                                              It refers to the process of mining for the Web page
site is facing increasing competition. E-commerce sites                                          content and back-office database, obtaining the useful
generate large amounts of data each day, and the data                                            knowledge from the Web docwnents, which also can carry
includes conswner-related potential information which is                                         out mining for the Web structure and link relationship to
valuable for market analysis and prediction [I] . So how to                                      obtain useful knowledge from artificial link structure.
effectively organize and use such business information and                                            2)     Web structure mining
how to understand customer interests and value preferences                                           Web structure includes the hyperlinks between different
as much as possible to optimize website design and provide                                       pages and the tree structure within the page which can be
users with personalized service, becomes the issues of the                                       expressed by HTML, XML, as well as the directory path
e-commerce to be solved urgently.                                                                structures in docwnent URL. Hyperlink structure between
    Web data mining is the application of data mining                                            the Web pages contains much useful information [2] .
technology in the Web environment, is the process to find
                                                                                                      3)     Web usage mining
the implicit, unknown potential value, non-trivial model
                                                                                                     The object of Web content mining, Web structure mining
from a large nwnber of Web docwnents and the data of
                                                                                                 is the original data in the network, but the Web usage mining
users visiting the website. Web data mining can play a role
                                                                                                 is different from the first two, which faces the second-hand
in many areas, and e-commerce provides a wealth of data
                                                                                                 data extracted from the process of user and network
sources and new research topic for the data mining.
                                                                                                 interactions. Web usage mining is to find the mode of users
                                                                                                 to access the Web pages through mining the Web log files
                         II.         WEB DATA MINING
                                                                                                 and the related data. It also can identify e-commerce
A.    Web data mining overview                                                                   potential customers through analyzing and exploring the
    Web mining is a technology to combine the data mining                                        principles in the Web log records, to strengthen the quality
technology and the Internet, simply said, Web mining is to                                       and delivery of end-user Internet information services, and
extract the interesting, potential useful patterns and hidden                                    improve the performance of the Web server system.

                                                                                                 B.
information from the Web docwnents and Web activities.
                                                                                                           Web data mining application in e-commerce
On Web there are a mass of data, so how to carry out
                                                                                                     The emergence of e-commerce has changed the
complex application of these data becomes the research
                                                                                                 traditional business models, also changed the relationship
hotspot of current database technology. Data mining is just
                                                                                                 between the vendors and customers. The expanded customer
to find the implicit content with regularity from large
                                                                                                 choice makes them pay more attention to the value of goods,
amounts of data to resolve data quality problems.
                                                                                                 but unlike the previous first consider the brand and
    At present, according to the direction of mining, Web
                                                                                                 geographical factors. Therefore, in terms of the seller to as
data mining is mainly divided into three categories: Web
                                                                                                 much as possible understand customer's tastes and values can
content mining, Web structure mining and Web usage
                                                                                                 remain invincible in the competition. Data mining
mining, as shown in figure 1.
                                                                                                 technology can effectively help sellers to understand


978-1-4244-5540-9/10/$26.00 ©2010 IEEE




                                                                                           719
customer behaviors and improve the efficiency of site, thus                   variety of mmmg techniques such as association rules,
having been widely used in the e-commerce design,                             service clustering and use clustering.
customer relationship management, Internet marketing, and                         Online recommendation module sets the recommendation
other fields.                                                                 engine in the Web server front-end, which combines the
                                                                              current browse activities of the user and the mined page
     III.     E-COMMERCE RECOMMENDATION SYSTEM DESIGN                         recommended set together to generate the corresponding
                           AND IMPLEMENTATION                                 recommendation set.
    The task of e-commerce recommendation system is to                            Model application module is to add the page links of the
observe the user's access request online, identify the user                   recommended set in the latest user request page, and then

seSSIOn set 0f each current on1·
                               me user [4] , to automatlca11y                 through the web server to deliver to the client browser [6] , to
    .                                                  .

recommend the pages they may be interested and have not                       provide users real-time personalized service; at the same time,
visited according to the user access pattern library                          the recommended results will be sent to the site management
established in the pattern mining module.                                     center to adjust the site design, optimize the site structure,
                                                                              thus to improve the site efficiency.
A.     System overall design                                                      The system applies the data mining ideas and methods to
    The system consists of five modules: data acquisition                     the Web server logs for Web usage mining, to extract the



     dU(
module, data preprocessing module, offline mining module,                     user's access law, and then comes the online access users
online recommendation module and model application                            down to a certain class, according to the law of such user's
mO    [ '''      ill fiT 2
                  i                    [ i]                                   access to carry out Web page recommendation, thus to
                                                                              provide users with personalized access.

                                                                              B.        System specific module design and implementation
                                                                                   1)     O.lfline module
                               Web Server
                                                                                  Offline module mainly consists of data pre-processing,
                                                                              Web mining and other modules, to provide support to the

                   3E
                                     _rr--
                                                       ]                      recommended engine, the system structure as shown in
                                                                              figure 3.


                                                                                                                  VL-                Rule data
                                                                                          Offline module
                                                                                                                  N-      ---
                                                                                                                                                       --



                                                                                                                                      -�
                                                                                                                                  Recommendation
                                                                                              '(7                                    algorithm

                                                                                           Rule model

                                                                                                             --
                                                                                                                                       lr
                                                                               '--

                                                                                                                                Accumulation of data

                                                                                                                                                   ---


                                                                                                                                       fF
            Figure 2.   E-commerce recommendation system based on Web
                                      mining                                                  '(7
                                                                                         Recommendation
    The main function of the data acquisition module is to                                                        �                Online module
collect the original log data to form data acquisition libraries                             engine
and prepare for future mining.
                                                                                                                  !--v
    Data pre-processing module is mainly to pre-process the                                           Figure 3.   Offline module structure
data in the data acquisition library to obtain a user
transaction database, and a user transaction is the set of a                      Data pre-processing and Web mining have relatively
group of meaningful browser pages of a user in a period of                    large time cost, can not meet the real time requirement of
time.                                                                         personalized recommendation services, so the two operations
    Offline mining module is mainly composed by the                           must be carried out offline, while the mining results can be
mining engine, mining algorithm library, pattern discovery                    directly referred by the online recommendation engine.
sub-module and pattern analysis sub-module. In which the                          Data preprocessing is to complete the work of two parts:
mining engine uses the data mining technology in the                          delete the data irrelevant to the data source (Web log file),
mining algorithm library to discover user patterns, through                   generate the user session documents and transaction database
the pattern analysis to analyze and interpret it to obtain                    useful for the mining stage and generate site data files for the
meaningful page recommendation set, this system provides a                    relevant documents in site. Pattern mining task is to use data




                                                                        720
mmmg algorithms to carry out mmmg for the result
transaction database of data preprocessing, to generate                                      Client                      Client
frequent user access page group, that is, user browse mode

library [3] . The offline part needs periodical mining to update
the user browse mode library.
    In general, the offline part of the recommended system                                                 Internet
mainly aims to registered users, is to clean up the
recommended set according to the key information users
provided, to display the precise information of user interest
in the recommend page set.                                                                               Web Server
  2)   Online module
    The online part mode is finally to provide customers
with the best quality of recommendation services when the

                                                                                                        Data interface
customer online browsing, so the design of the online part is
crucial for the whole recommendation system.
    Online part is composed by the pattern analysis tasks and
the data source acquisition tasks. In which the pattern
analysis tasks use the offline part to generate results and then                 Recommended
online recommend the next step operation for users                                 result set                  Recommendation engine
according to the current access behaviors of users, which
consists of personalized recommendation engine and Web
servers, personalization recommendation engine is to find
the user's interest through analyzing the user's current access
operation, and generate recommendation results through the

                                                                                                        Web database
recommendation algorithms, thus to achieve personalized
services. The acquisition task of the data source is to collect
the Web log files on Web server, and this process is                            Figure 4.   shows the structure diagram of the system online part
automatically completed by configuring the server's
corresponding parameters.                                                    From the above figure it can be seen, when users visit the
    Online mode is divided into two situations, one is for               Web site the web page request will be first sent to the Web
registered user login, second is for random non-registered               server to be submitted to the recommendation engine module,
users. Registered users can change any interest, while the               and then the latter will use the pattern library the offline
recommendation system based on the user's choice will form               module generated and Web database to calculate, generate
a recommended set and display the precise recommendation                 recommendation result set to be returned to the Web server
pages, and if the changed interest term does not include the             to return to the client through Http protocol, finally displayed
key information of the registered users, then the                        in the browser. Thus the whole recommendation process is
recommended set will be generated from the original                      achieved. And from this process it can be seen that the entire
recommended set, so that the recommendation page users                   calculation process is completed by the recommendation
obtained will be more accurate.                                          engine.
    For non-registered users, the recommended page is
closely related to the user interest, while the precise extent                                    IV.     CONCLUSION
largely depends on the user interest item selection, that is,                At present, Web data mining has gradually become a hot
the more the user interest item constraints the more accurate            issue in network research, data mining, knowledge discovery,
the recommended page [5] , but this has the cost of the user             software agent, and other fields. Log mining research for
time consumption. The first page of random users includes                Web site optimization, e-commerce, distant education,
all the categories and types. And after a series of choices, the         information retrieval and other fields has great significance.
recommendation      system   will  finally   generate    a               However, how to complete these technologies and as soon as
recommended page, and the more the selected interest items,              possible use them in a variety of Internet applications is a
the more precise the recommended page.                                   new topic we faced.
                                                                             This paper studies the e-commerce recommendation
                                                                         system models based on Web data mining, designs a
                                                                         personalized recommendation system based on Web use
                                                                         mining for e-commerce, and discusses in detail the function
                                                                         of each module of the system and implementation techniques.
                                                                         The system can access to the user browsing patterns to
                                                                         provide personalized service for users through the mining of
                                                                         the historical log data of Web server.




                                                                   721
REFERENCES
[I].   [Mobasher, B. Dai, Improving the Effectiveness of Collaborative
       Filtering on Anonymous Web Usage Data, ITWPOI, 2001.
[2]. Zhen Liu, Minyi Guo. A Proposal of Integrating Data Mining and
     On-line Analytical Processing in Data Warehouse, In Proceeding of
     international conference on mechatronics and information technology,
     2001:146-51.
[3].   Sarwar B.Karypis, G.Konstan, J., and Riedl, J. Analysis of
       Recommendation Algorithms for E-Commerce, ACM Conference on
       Electronic Commerce, 2000.
[4].   N.Colossi, W.Malloy, B. Reinwald Relational extensions For OALP,
       IBM Systems Journal [J].2002, 41(4):31-35.
[5]. CHEN Yu-ru, HUNG Ming-chuan, Don-lin YANG, Using Data
     Mining to Construct an Intelligent Web Search System [1],
     International Journal of Computer Processing of Oriental Languages,
     2003.
[6].   S.Foucaud, A.Zanichelli, B.Garilli, M. Scodeggio and P.Franzetti.
       VIPGI and Elise3D: Reducing VIMOS-IFU Data and Searching for
       Emission Line Sources in Data Cubes. New Astronomy Reviews, In
       Press, Corrected Proof, and Available online.19 April 2006.




                                                                            722

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  • 1. Research of Data Mining Based on E-Commerce Liu Xiao-liang Li Yong-hong, Dept. of Business Hebei University of Economics and Business Dept. of Account Hebei University of Economics and Shijiazhuang, Hebei Business lx1130225@163.com Shijiazhuang, Hebei liyonghong 1966@126.com Abs1l'llcf-this paper describes the Web data mining teehnology, Web data mining process, data sources and uses, designs a personalized recommendation system based on Web use mining for e-commerce, and discusses in detail the function of each module of the system and implementation techniques. Through the mining of the historical log data of Web server, l'Cb mining Web usage mining the system can access to the user browsing patterns to provide personalized service for users. KI!)'H'01'fIs-IYeIJ tiotfl lIIining; e-ctJlIII1II!1'Ce; recollllllt!lllltllll SJ'SIelll Figure I. Web mining classification I. INTRODUCTION With the increasing popularity of Internet and the rapid J) Web content mining development of e-commerce, internet-based business Web It refers to the process of mining for the Web page site is facing increasing competition. E-commerce sites content and back-office database, obtaining the useful generate large amounts of data each day, and the data knowledge from the Web docwnents, which also can carry includes conswner-related potential information which is out mining for the Web structure and link relationship to valuable for market analysis and prediction [I] . So how to obtain useful knowledge from artificial link structure. effectively organize and use such business information and 2) Web structure mining how to understand customer interests and value preferences Web structure includes the hyperlinks between different as much as possible to optimize website design and provide pages and the tree structure within the page which can be users with personalized service, becomes the issues of the expressed by HTML, XML, as well as the directory path e-commerce to be solved urgently. structures in docwnent URL. Hyperlink structure between Web data mining is the application of data mining the Web pages contains much useful information [2] . technology in the Web environment, is the process to find 3) Web usage mining the implicit, unknown potential value, non-trivial model The object of Web content mining, Web structure mining from a large nwnber of Web docwnents and the data of is the original data in the network, but the Web usage mining users visiting the website. Web data mining can play a role is different from the first two, which faces the second-hand in many areas, and e-commerce provides a wealth of data data extracted from the process of user and network sources and new research topic for the data mining. interactions. Web usage mining is to find the mode of users to access the Web pages through mining the Web log files II. WEB DATA MINING and the related data. It also can identify e-commerce A. Web data mining overview potential customers through analyzing and exploring the Web mining is a technology to combine the data mining principles in the Web log records, to strengthen the quality technology and the Internet, simply said, Web mining is to and delivery of end-user Internet information services, and extract the interesting, potential useful patterns and hidden improve the performance of the Web server system. B. information from the Web docwnents and Web activities. Web data mining application in e-commerce On Web there are a mass of data, so how to carry out The emergence of e-commerce has changed the complex application of these data becomes the research traditional business models, also changed the relationship hotspot of current database technology. Data mining is just between the vendors and customers. The expanded customer to find the implicit content with regularity from large choice makes them pay more attention to the value of goods, amounts of data to resolve data quality problems. but unlike the previous first consider the brand and At present, according to the direction of mining, Web geographical factors. Therefore, in terms of the seller to as data mining is mainly divided into three categories: Web much as possible understand customer's tastes and values can content mining, Web structure mining and Web usage remain invincible in the competition. Data mining mining, as shown in figure 1. technology can effectively help sellers to understand 978-1-4244-5540-9/10/$26.00 ©2010 IEEE 719
  • 2. customer behaviors and improve the efficiency of site, thus variety of mmmg techniques such as association rules, having been widely used in the e-commerce design, service clustering and use clustering. customer relationship management, Internet marketing, and Online recommendation module sets the recommendation other fields. engine in the Web server front-end, which combines the current browse activities of the user and the mined page III. E-COMMERCE RECOMMENDATION SYSTEM DESIGN recommended set together to generate the corresponding AND IMPLEMENTATION recommendation set. The task of e-commerce recommendation system is to Model application module is to add the page links of the observe the user's access request online, identify the user recommended set in the latest user request page, and then seSSIOn set 0f each current on1· me user [4] , to automatlca11y through the web server to deliver to the client browser [6] , to . . recommend the pages they may be interested and have not provide users real-time personalized service; at the same time, visited according to the user access pattern library the recommended results will be sent to the site management established in the pattern mining module. center to adjust the site design, optimize the site structure, thus to improve the site efficiency. A. System overall design The system applies the data mining ideas and methods to The system consists of five modules: data acquisition the Web server logs for Web usage mining, to extract the dU( module, data preprocessing module, offline mining module, user's access law, and then comes the online access users online recommendation module and model application down to a certain class, according to the law of such user's mO [ ''' ill fiT 2 i [ i] access to carry out Web page recommendation, thus to provide users with personalized access. B. System specific module design and implementation 1) O.lfline module Web Server Offline module mainly consists of data pre-processing, Web mining and other modules, to provide support to the 3E _rr-- ] recommended engine, the system structure as shown in figure 3. VL- Rule data Offline module N- --- -- -� Recommendation '(7 algorithm Rule model -- lr '-- Accumulation of data --- fF Figure 2. E-commerce recommendation system based on Web mining '(7 Recommendation The main function of the data acquisition module is to � Online module collect the original log data to form data acquisition libraries engine and prepare for future mining. !--v Data pre-processing module is mainly to pre-process the Figure 3. Offline module structure data in the data acquisition library to obtain a user transaction database, and a user transaction is the set of a Data pre-processing and Web mining have relatively group of meaningful browser pages of a user in a period of large time cost, can not meet the real time requirement of time. personalized recommendation services, so the two operations Offline mining module is mainly composed by the must be carried out offline, while the mining results can be mining engine, mining algorithm library, pattern discovery directly referred by the online recommendation engine. sub-module and pattern analysis sub-module. In which the Data preprocessing is to complete the work of two parts: mining engine uses the data mining technology in the delete the data irrelevant to the data source (Web log file), mining algorithm library to discover user patterns, through generate the user session documents and transaction database the pattern analysis to analyze and interpret it to obtain useful for the mining stage and generate site data files for the meaningful page recommendation set, this system provides a relevant documents in site. Pattern mining task is to use data 720
  • 3. mmmg algorithms to carry out mmmg for the result transaction database of data preprocessing, to generate Client Client frequent user access page group, that is, user browse mode library [3] . The offline part needs periodical mining to update the user browse mode library. In general, the offline part of the recommended system Internet mainly aims to registered users, is to clean up the recommended set according to the key information users provided, to display the precise information of user interest in the recommend page set. Web Server 2) Online module The online part mode is finally to provide customers with the best quality of recommendation services when the Data interface customer online browsing, so the design of the online part is crucial for the whole recommendation system. Online part is composed by the pattern analysis tasks and the data source acquisition tasks. In which the pattern analysis tasks use the offline part to generate results and then Recommended online recommend the next step operation for users result set Recommendation engine according to the current access behaviors of users, which consists of personalized recommendation engine and Web servers, personalization recommendation engine is to find the user's interest through analyzing the user's current access operation, and generate recommendation results through the Web database recommendation algorithms, thus to achieve personalized services. The acquisition task of the data source is to collect the Web log files on Web server, and this process is Figure 4. shows the structure diagram of the system online part automatically completed by configuring the server's corresponding parameters. From the above figure it can be seen, when users visit the Online mode is divided into two situations, one is for Web site the web page request will be first sent to the Web registered user login, second is for random non-registered server to be submitted to the recommendation engine module, users. Registered users can change any interest, while the and then the latter will use the pattern library the offline recommendation system based on the user's choice will form module generated and Web database to calculate, generate a recommended set and display the precise recommendation recommendation result set to be returned to the Web server pages, and if the changed interest term does not include the to return to the client through Http protocol, finally displayed key information of the registered users, then the in the browser. Thus the whole recommendation process is recommended set will be generated from the original achieved. And from this process it can be seen that the entire recommended set, so that the recommendation page users calculation process is completed by the recommendation obtained will be more accurate. engine. For non-registered users, the recommended page is closely related to the user interest, while the precise extent IV. CONCLUSION largely depends on the user interest item selection, that is, At present, Web data mining has gradually become a hot the more the user interest item constraints the more accurate issue in network research, data mining, knowledge discovery, the recommended page [5] , but this has the cost of the user software agent, and other fields. Log mining research for time consumption. The first page of random users includes Web site optimization, e-commerce, distant education, all the categories and types. And after a series of choices, the information retrieval and other fields has great significance. recommendation system will finally generate a However, how to complete these technologies and as soon as recommended page, and the more the selected interest items, possible use them in a variety of Internet applications is a the more precise the recommended page. new topic we faced. This paper studies the e-commerce recommendation system models based on Web data mining, designs a personalized recommendation system based on Web use mining for e-commerce, and discusses in detail the function of each module of the system and implementation techniques. The system can access to the user browsing patterns to provide personalized service for users through the mining of the historical log data of Web server. 721
  • 4. REFERENCES [I]. [Mobasher, B. Dai, Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data, ITWPOI, 2001. [2]. Zhen Liu, Minyi Guo. A Proposal of Integrating Data Mining and On-line Analytical Processing in Data Warehouse, In Proceeding of international conference on mechatronics and information technology, 2001:146-51. [3]. Sarwar B.Karypis, G.Konstan, J., and Riedl, J. Analysis of Recommendation Algorithms for E-Commerce, ACM Conference on Electronic Commerce, 2000. [4]. N.Colossi, W.Malloy, B. Reinwald Relational extensions For OALP, IBM Systems Journal [J].2002, 41(4):31-35. [5]. CHEN Yu-ru, HUNG Ming-chuan, Don-lin YANG, Using Data Mining to Construct an Intelligent Web Search System [1], International Journal of Computer Processing of Oriental Languages, 2003. [6]. S.Foucaud, A.Zanichelli, B.Garilli, M. Scodeggio and P.Franzetti. VIPGI and Elise3D: Reducing VIMOS-IFU Data and Searching for Emission Line Sources in Data Cubes. New Astronomy Reviews, In Press, Corrected Proof, and Available online.19 April 2006. 722