user behaviour analysis

1. Submitted By:- Vaibhav
9910103585(F3)
JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA

2. Introduction
Personalization of Web Information Retrieval Based
upon User Behavior Modeling and Relevance
Extraction
User tracking + Analyzing details = User Behavior
Analysis

3. Aim
To make good relationship
Track the user online
User behavior tracking

4. Sources of Research papers
 Scholars At Google
 Pintrest
 Flipcart R&D
 Acm
 Springer
 IEEE

6. Summary of research work
 There 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

7. 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

8. Paper 3
Active page
Passive page
Dead page
Surfing page

9. Diagrammatical Representation of Problem

10. Other Approaches
. Coordinates
. Snapshots chalkmark
(http://www.optimalworkshop.com/chalkmark.htm)
. Eularian distance

11. Tools & Technology
 Microsoft Visual Studio
 JQuery & Ajax
 Google Search API
 PHP
 LIB SVM Classifier
 Wempserver
 JSON API
 Mysql
 Filezila
 Python
 C++

12. Algorithm

13. Experimental studies
Answer the user accordingly.

14. Problem Statement
Try to give the user the things they wants.
Solution
Track 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.

15. Requirement specification
Purpose: 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.
 Polymorphism
 Inheritance
 Encapsulation.

16. Non Functional Requirement
 Security: 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.

17. Design Model

18. Risk Analysis
 Slow working
 In compatible
 Load on the server
 Performance Decrease
 Low security

19. Implementation

22. Result

23. 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.

24. Thanks..