constructing web user profiles

8/4/2019 Constructing Web User Profiles

http://slidepdf.com/reader/full/constructing-web-user-profiles 1/15

Constructing Web User ProfilesConstructing Web User Profiles



Web User Profiles consistWeb User Profiles consist

y Page Interest Estimators (PIE)

y WebAccess Graphs (WAG)



Page Interest Estimators (PIE)Page Interest Estimators (PIE)

y We can identify the patterns of pages that

constitute user·s interest.

y We can apply learning algorithm to induce

classifiers that predict if a page is of interest to

the user. These classifiers are called Page

Interest Estimators (PIE).



Web Access Graph (WAG)Web Access Graph (WAG)

y In addition to the Page Interest Estimator

(PIE), our user profile contains a Web

Access Graph (WAG). A WAG is a

weighted directed graph that represents auser·s access behavior.



What is Web Mining ?What is Web Mining ?

y Web mining - is the application of data

mining techniques to discover patterns

from the Web.

y Web usage mining

y Web content mining

y Web structure mining



Web usage mining Web usage mining

Web usage mining is a process of extracting

useful information from server logs i.e

users history. Web usage mining is the

process of finding out what users arelooking for on the Internet. Some users

might be looking at only textual data,

whereas some others might be interestedin multimedia data.



Web content mining Web content mining

y Web content mining is the process to

discover useful information from text,

image, audio or video data in the web.

y Web content mining sometimes is called

web text mining, because the text content

is the most widely researched area.



Web structure mining Web structure mining

y Web structure mining is the process of using graph theory to

analyze the node and connection structure of a web site. According

to the type of web structural data, web structure mining can be

divided into two kinds:

y 1. Extracting patterns from hyperlinks in the web: a hyperlink is a

structural component that connects the web page to a different

location.

y 2. Mining the document structure: analysis of the tree-like structure

of page structures to describe HTML or XML tag usage.



ClusteringClustering

y Clustering techniques can be used to find pages

that are closely associated with each other and

are likely to be accessed by the user

consecutively.



Utilization of user profileUtilization of user profile

y Analysis of search results.

y Recommendations of new and interesting

pages



AlgorithmAlgorithm

y User profile registration

y User visits

y Total number of visit·s on each page

y Insert visit records into MYSQL database

y Preparing and processing data

y Page interest Estimators (PIE).

y Web Access Graphs (WAG).y Clustering

y Recommendations of new and interesting pages.



DiagramDiagram

User Site

Database

Data

Processing

User Visits

Data Collection

Results



Software & Hardware requirementsSoftware & Hardware requirements

y Software :

x WAMP Server

y Hardware :

x Linux Server

y Frontend :x PHP

x Our frontend is PHP. We are using PHP script for processingour algorithm, data collection, insert data into database andto display the results.

y Backend :x MYSQL

x Our backend is MYSQL. We are using MYSQL to storecollected data



Advantages :Advantages :

y User can easily find what they are searching for.

y Saves time of user.

y It makes system more intelligent.



REFERENCESREFERENCES

y Aciar, S., Zhang, D., Simoff, S., & Debenham, J. (2007). Informed

recommender: Basing recommendations on consumer

product reviews. IEEE Intelligent Systems,22(3), 39²47.

y Bettman , J. R., & Park, C. W. (1980). Effects of prior

knowledge and experience and phase of the choice processon consumer decision processes: A protocol analysis. Journal

of Consumer Research, 7(December), 234²248.

y Canter, D., Rivers, R., & Storrs, G. (1985). Characterising users

navigation through complex data structures. Behaviour and

Information Technology, 4(2), 93²102.y Lee, M. G. (2001). Profiling students· adaptation styles in web-based

learning.Computers & Education, 36, 121²132.

constructing web user profiles

Documents