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Reseach Article

User Action Interpretation for Online Content Optimization

Published on December 2014 by Leena Dhangar, A. D. Potgantwar
Innovations and Trends in Computer and Communication Engineering
Foundation of Computer Science USA
ITCCE - Number 1
December 2014
Authors: Leena Dhangar, A. D. Potgantwar
027daa5d-4a2d-435f-9101-43ca2cdae996

Leena Dhangar, A. D. Potgantwar . User Action Interpretation for Online Content Optimization. Innovations and Trends in Computer and Communication Engineering. ITCCE, 1 (December 2014), 13-16.

@article{
author = { Leena Dhangar, A. D. Potgantwar },
title = { User Action Interpretation for Online Content Optimization },
journal = { Innovations and Trends in Computer and Communication Engineering },
issue_date = { December 2014 },
volume = { ITCCE },
number = { 1 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 13-16 },
numpages = 4,
url = { /proceedings/itcce/number1/19040-2004/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations and Trends in Computer and Communication Engineering
%A Leena Dhangar
%A A. D. Potgantwar
%T User Action Interpretation for Online Content Optimization
%J Innovations and Trends in Computer and Communication Engineering
%@ 0975-8887
%V ITCCE
%N 1
%P 13-16
%D 2014
%I International Journal of Computer Applications
Abstract

Search engine advertising has become a significant element of the Web browsing experience. Choosing the right items for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each item. This ranking has a strong impact on the revenue the search engine receives from the clicking on advertisements. Displaying the items to the user that they prefer to click on improves user satisfaction. Therefore, it is important to be able to accurately estimate the click-through rate of ads in the system. So the user's experience depends crucially upon the quality of content recommendations. This paper presents an overview of the content recommendation, namely how to recommend a small set of items to a user from an underlying pool of content items according to user's interest. Therefore, we build an online learning framework for personalized recommendation using recommender system. This paper focuses on an approach of interpreting users' actions for the online learning to achieve better item relevance estimation. So that User is provided with the content in which he is interested. And finally the items are ranked according to the user's interest based on the click through rate (CTR).

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Index Terms

Computer Science
Information Sciences

Keywords

Content Recommendation Recommender System Click Through Rate (ctr)