CFP last date
21 October 2024
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).

References
  1. Jiang Bian, Anlie Dong, Xiaofeng He, Srihari Reddy, Yi Chang, "User Action Interpretation for Online Content Optimization, VOL. 25, No. 9,2013.
  2. L. M. de Campos, J. M. Ferndez-Luna, J. F. Huete, and M. A. Rueda Morales, "Combining Content Based filtering and Collaborative Recommendations: A Hybrid Approach Based on Bayesian Networks ",Int'l Approximate Reasoning vol. 51, no. 7, pp. 785-799, 2010.
  3. D. Agarwal, B- C. Chen, and P. Elango, "Fast Online Learning through Offline Initialization for Time Sensitive Recommendations" Proc. 16th ACM SIGKDD Int'l Cont. Knowledge Discovery and Data Mining (KDD), 2010.
  4. D. Agarwal, B- C. Chen, and P. Elango, "Spatio-Temporal Models for Estimating Click- Through Rate" Proc. 18th Int'l World Wide Web Cont. (WWW), 2009.
  5. Agarwal, B- C. Chen, P. Elango, N. Motgi, S-T. Park, R Ramakrishnan, S. Roy, and J. Zachariah," Online Models for Content Optimization. " 2005.
  6. G. Buscher, L. vainest and Dengel, "Segmentation Level Display Time as Implicit Feedback: A Comparison to Eye Tracking" , Proc. 32nd Ann. Int'l ACM SIGIR Cont. Research and Development in Information Retrieval (SIGIR), 2009.
  7. W. Chu and Z. Ghahramani, "Probabilistic Models for Incomplete Multi- Dimensional Arrays", Proc. 12th Int'l Conf. Artificial Intelligence and Statistics, 2009.
  8. W. Chu, S. T. Park, "Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models ", Proc. 18th Int'l World Wide Web Conf. (WWW), 2009.
  9. W. Chu, S. T. Park, T. Beaupre, N. Motgi and A. Phadke, "A Case Study Of Behaviour- Driven Co joint Analysis on Yahoo! Front page Today Module", Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2009.
  10. D. Downey, S. Dumais, D. Liebling, and E. Horvitz, "Under-standing The Relationship between Searchers' Queries and Information Goals," Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), 2008.
  11. J. Ahn, P. Brusilovsky, J. Grady, D. He, and S. Y. Syn, "Open User Profiles for Adaptive News Systems: Help or Harm?" Proc. 16th Int'l World Wide Web Conf. (WWW), 2007.
  12. Z. Guan and E. Cutrell, "An Eye Tracking Study of the Effect of Target Rank on Web Search," Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI), 2007.
  13. A. Broder, "Computational Advertising and Recommender Systems", Proc. Second ACM Int'l Conf. Recommender System (RecSys), 2007.
  14. D. Billsus and M. Pazzani, "Adaptive News Access- the Adaptive web Methods and Strategies of web Personalization", Springer-Verlag 2007.
  15. E. Gabrilovich, S. Dumais, and E. Horvitz, "Newsjunkie: Providing Personalized Newsfeeds via Analysis of Information Novelty," Proc. 13th Int'l World Wide Web Conf. (WWW), 2004.
  16. L. A. Granka, T. Joachims, and G. Gay, "Eye-Tracking Analysis of User Behaviour in WWW Search," Proc. 29th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2004.
  17. R. Jin, J. Y. Chai, and L. Si, "An Automatic Weighting Scheme for Collaborative Filtering," Proc. 29th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2004.
  18. A. Das, M. Datar, A. Garg, S. Rajaram, "Google News Personalization Scalable Online Collaborative Filtering", Proc. 16th Int'l World Wide Web Cont. (WWW), 2004.
  19. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, "An Algorithmic Framework for Performing Collaborative Filtering," Proc. 29th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 1999.
  20. T. Hofmann and J. Puzicha, "Latent Class Models for Collaborative Filtering," Proc. 16th Int'l Joint Conf. Artificial Intelligence (IJCAI), 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Content Recommendation Recommender System Click Through Rate (ctr)