User Behavior Analysis

Submitted By:- Vaibhav9910103585(F3)

JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA

IntroductionPersonalization of Web Information Retrieval Based upon User Behavior Modeling and Relevance Extraction

User tracking + Analyzing details = User Behavior Analysis

Aim

To make good relationship

Track the user online

User behavior tracking

Sources of Research papersScholars At GooglePintrestFlipcart R&D AcmSpringerIEEE

Summary of research workThere are two approaches /mode which are

used to show the result on the serp page. A. The results are shown according to the

relevance.B. The results are shown according to the

revenue and the user behavior CTR

Paper 2 . Statical Features: a user will click a url if he

examine the URL relevant(Depend=?)

Click-Model Features: the statical features are having arbitrary nature because it is query

and the URL dependent

Paper 3Active pagePassive pageDead pageSurfing page

Diagrammatical Representation of Problem

Other Approaches

. Coordinates

. Snapshots chalkmark (http://www.optimalworkshop.com/chalkmark.htm)

. Eularian distance

Tools & TechnologyMicrosoft Visual StudioJQuery & AjaxGoogle Search APIPHPLIB SVM ClassifierWempserverJSON APIMysqlFilezilaPythonC++

Algorithm

Experimental studies

Answer the user accordingly.

Problem StatementTry to give the user the things they wants.

SolutionTrack the user and tried to judge the requirement of the user more clearly.

Show the related things of the user interest and best possible alternative.

Requirement specificationPurpose: The purpose of the project is to

provide the owner of the webpage about the details of the person who visited the owner websites.

Must be a fast system.Dynamic capturing the user’s details.PolymorphismInheritanceEncapsulation.

Non Functional RequirementSecurity: The product is secured.Reliability: The product is reliable.Efficiency: The product is efficient.Portability: The product is portable as it is

available online and can be accessed from anywhere.

Maintainability: The product is easily maintained as it is global.

Design Model

Risk AnalysisSlow workingIn compatibleLoad on the serverPerformance DecreaseLow security

Implementation

Result

References [1] E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for

predicting web search result preferences. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR), pages 3–10, 2006.

[2] M. Beal and Z. Ghahraman. Variational Bayesian learning of directed graphical models with hidden variables. Bayesian Analysis, 1(4):793–832, 2006.

[3] A. Broder. A taxonomy of web search. SIGIR Forum, 36(2):3–10, 2002.

[4] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the

22nd international conference on Machine learning, pages 89–96, 2005.

[5] Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon. Adapting ranking svm to document retrieval. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in informat ion retrieval, 2006.

[6] B. Carlin and T. Louis. Bayes and Empirical Bayes Methods for Data Analysis. Chapman & Hall/CRC, 2000.

Thanks..