Submitted By:- Vaibhav
JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA
Personalization of Web Information Retrieval Based
upon User Behavior Modeling and Relevance
User tracking + Analyzing details = User Behavior
To make good relationship
Track the user online
User behavior tracking
Sources of Research papers
Scholars At Google
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
. 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
Answer the user accordingly.
Try to give the user the things they wants.
Track the user and tried to judge the requirement of the
user more clearly.
Show the related things of the user interest and best
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.
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
 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.
 M. Beal and Z. Ghahraman. Variational Bayesian learning of directed graphical models with
hidden variables. Bayesian Analysis, 1(4):793–832, 2006.
 A. Broder. A taxonomy of web search. SIGIR Forum, 36(2):3–10, 2002.
 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.
 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.
 B. Carlin and T. Louis. Bayes and Empirical Bayes Methods for Data Analysis. Chapman &