CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Web Personalization Systems and Web Usage Mining: A Review

by Rajesh Shukla, Sanjay Silakari, P K Chande
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 21
Year of Publication: 2013
Authors: Rajesh Shukla, Sanjay Silakari, P K Chande
10.5120/12664-9264

Rajesh Shukla, Sanjay Silakari, P K Chande . Web Personalization Systems and Web Usage Mining: A Review. International Journal of Computer Applications. 72, 21 ( June 2013), 6-13. DOI=10.5120/12664-9264

@article{ 10.5120/12664-9264,
author = { Rajesh Shukla, Sanjay Silakari, P K Chande },
title = { Web Personalization Systems and Web Usage Mining: A Review },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 21 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number21/12664-9264/ },
doi = { 10.5120/12664-9264 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:30.425544+05:30
%A Rajesh Shukla
%A Sanjay Silakari
%A P K Chande
%T Web Personalization Systems and Web Usage Mining: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 21
%P 6-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, the field of web personalization is growing exponentially. From e-mail, e-trading, internet forum to social networking based websites, directly or indirectly utilize web personalization and recommendation system for providing customized services to their loyal users. Personalization is achieved through web mining, i. e. extracting knowledge from the collected data. Knowledge is then filtered and processed to model user behavior that forms the basis of a personalized system. This paper presents a brief review of recent research efforts in web personalization and recommendation by means of web usage mining, for the benefit of research in this area. It also elaborates the role of web usage mining in personalization, and presents the open challenges that are yet to be met.

References
  1. A. Vaishnavi, "Effective Web personalization system using Modified Fuzzy Possibilistic C Means," Bonfring International Journal of Software Engineering and Soft Computing, Vol. 1, Special Issue, 2011, pp. 1-7.
  2. Anja Feldmann, "Continuous online extraction of HTTP traces from packet traces," In Proc. of World Wide Web Consortium Workshop on Web Characterization, 1998.
  3. Anup Prakash Warade , Vignesh Murali Natarajan and Siddharth Sharad Chandak, "How to Develop Online Recommendation Systems that Deliver Superior Business Performance," Cognizant 20-20 Insights.
  4. Bamshad Mobasher, Robert Cooley and Jaideep Srivastava, "Automatic personalization based on Web usage mining," Communications of the ACM, Vol. 43, No. 8, 1997, pp. 142-151.
  5. Bhushan Shankar Suryavanshi, Nematollaah Shiri and Sudhir P. Mudur, "Incremental Relational Fuzzy Subtractive Clustering for Dynamic Web Usage Profiling," In Proc. of ACM Workshop on Knowledge Discovery in the Web, 2005.
  6. Bin Xu, Jiajun Bu, Chun Chen and Deng Cai, "An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups," In Proc. of the IEEE 21st international conference on World Wide Web, ACM, 2012, pp. 21-30.
  7. Byeong Man Kim, Qing Li, Jong-Wan Kim, and Jinsoo Kim, "A New Collaborative Recommender System Addressing Three Problems," Springer, LNCS, Vol. 3157, 2004, pp 495-504.
  8. C. Shahabi, A. Zarkesh, M. Abidi, J. and V. Shah, "Knowledge discovery from users Web-page navigation," In Proc. of IEEE 7th International Workshop on Research Issues in Data Engineering, 1997, pp. 20-29.
  9. Dimitrios Pierrakos, Georgios Paliouras and Yannis Ioannidis, "OurDMOZ: A System for Personalizing the Web," In Proc. of 6th International Workshop on Personalized Access, Profile Management, and Context Awareness in Databases, 2012.
  10. Dimitrios Pierrakos, Georgios Paliouras, Christos Papatheodorou and Constantine D. Spyropoulos, "Web Usage Mining as a Tool for Personalization: A Survey," ACM, User Modeling and User-Adapted Interaction, Vol. 13, No. 4, 2003, pp. 311-372.
  11. Georgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis and Constantine D. Spyropoulos, "Clustering the Users of Large Web Sites into Communities," In Proc. of 7th International Conference on Machine Learning, 2000, pp. 719-726.
  12. http://wordpress. org/extend/plugins/wp-greet-box/
  13. http://www. adobe. com/products/testandtarget. html
  14. http://www. ariadne. ac. uk/issue28/personalization
  15. http://www. prnewswire. com/news-releases/magiq-the-on-demand-digital-marketing-company-launches-with-website-content-personalization-and-trigger-based-online-marketing-services-for-marketers-70273237. html
  16. http://www. w3c. org/P3P/
  17. http://www-142. ibm. com/software/products/us/en/personalized-product-recommendations/
  18. Ilham Esslimani, Armelle Brun and Anne Boyer, "Densifying a behavioral recommender system by social networks link prediction methods," Springer, Social Network Analysis and Mining, Vol. 1, No. 3, 2011, pp. 159-172.
  19. J. Han and Kamber, "DataMining: Concepts and Techniques," Morgan Kaufmann Publishers.
  20. Jaideep Srivastava, Robert Cooley, Mukund Deshpande and Pang-Ning Tan, "Web usage mining: discovery and applications of usage patterns from Web data," ACM SIGKDD Explorations Newsletter, Vol. 1, No. 2, 2000, pp. 12-23.
  21. Jose M. Domenech and Javier Lorenzo, "A Tool for Web Usage Mining," In Proc. of 8th International Conference on Intelligent Data Engineering and Automated Learning , 2007.
  22. Joseph A. Konstan and John Riedl, "Recommender systems: from algorithms to user experience," Springer, User Modeling and User-Adapted Interaction, Vol. 22, No. 1-2, pp. 101-123.
  23. K. Vinodh and Saji K Mathew, "Web Personalization in Technology Acceptance," in Proc. of IEEE 4th International Conference on Intelligent Human Computer Interaction, 2012, pp. 1-6.
  24. Karen H. L. TsoSutter, Leandro Balby Marinho and Lars Schmidt-Thieme, "Tag-aware recommender systems by fusion of collaborative filtering algorithms," In Proc. of 2008 ACM symposium on Applied computing, 2008, pp. 1995-1999.
  25. M. D. Mulvenna and A. G. Buchner, "Data mining and electronic commerce," In Proc. of Overcoming Barriers to Electronic Commerce, 1997, pp. 1-7.
  26. Magdalini Eirinaki and Michalis Vazirgiannis, "Web Mining for Web Personalization," ACM Transactions on Internet Technology, Vol. 3, No. 1, 2003, pp. 2-38.
  27. Michael Chau and Hsinchun Chen, "A machine learning approach to web page filtering using content and structure analysis," Elsevier, Decision Support Systems, Vol. 44, No. 2, 2008, pp. 482-494.
  28. Murat Ali Bayir, Ismail Hakki Toroslu, Ahmet Cosar and Guven Fidan "Discovering more accurate Frequent Web Usage Patterns," arXiv0804. 1409v1, 2008.
  29. Myra Spiliopoulou, Carsten Pohle and Lukas C. Faulstich, "Improving the effectiveness of a web site with Web usage mining," Springer, LNCS, Vol. 1836, 2000, pp. 142-162.
  30. Oren Etzioni, "The World Wide Web: Quagmire or gold mine," Communications of the ACM, Vol. 39, No. 11, pp. 65-68.
  31. R. Cooley, B. Mobasher and J. Srivastava, "Grouping Web page references into transactions for mining World Wide Web browsing patterns," Technical Report TR 97-021. Dept. of Computer Science, Univ. of Minnesota, Minneapolis, USA, 1997.
  32. R. Kosala and H. Blockeel. , "Web mining research: a survey," ACM SIGKDD Explorations Newsletter, Vol. 2, No. 1, pp. 1-15.
  33. Rakesh Agrawal and Ramakrishnan Shrikant, "Fast Algorithms for Mining Association Rules in Large Databases," In Proc. of 20th International Conference on Very Large DataBases, 1994, pp. 487-499.
  34. Rana Forsati, Mohammad Reza Meybodi and Afsaneh Rahbar, "An Efficient Algorithm for Web Recommendation Systems," In Proc. of IEEE/ACS International Conference on Computer Systems and Applications, May 2009, pp. 579-586.
  35. Ranieri Baraglia, Claudio Lucchese, Salvatore Orlando, Massimo Serrano and Fabrizio Silvestri, "A privacy preserving web recommender system," In Proc. of ACM symposium on Applied computing, 2006, pp. 559-563.
  36. Ronaldo Lima Rocha Campos, Rafaela Lunardi Comarella and Ricardo Azambuja Silveira, "Multiagent Based Recommendation System Model for Indexing and Retrieving Learning Objects," Springer, Communications in Computer and Information Science Vol. 365, 2013, pp. 328-339.
  37. Samira Khonsha and Mohammad Hadi Sadreddini, "New hybrid web personalization framework," In Proc. of IEEE 3rd International Conference on Communication Software and Networks, 2011, pp. 86-92.
  38. Sarabjot Singh Anand and Bamshad Mobasher "Intelligent Techniques for Web Personalization," LNCS, Vol. 3169, 2005, Springer, pp. 1-36.
  39. Tamas Jambor, Jun Wang and Neal Lathia, "Using Control Theory for Stable and Efficient Recommender Systems," In Proc. of The 21st International Conference on World Wide Web, 2012, pp. 11-20.
  40. Tom Mitchell, "Machine Learning," McGraw-Hill
  41. U. Manber, A. Patel and J. Robison, "Experience with Personalization on Yahoo!," Communications of the ACM, Vol. 43, No. 8, August 2000, pp. 35-39.
  42. Utpala Niranjan, Dr. V. V. Krishna, Kanduri Srividya and Dr. V. Khanaa, "Developing a Dynamic web recommendation System based on Incremental Data Mining," In Proc. of IEEE 3rd International Conference on Electronics Computer Technology, 2011, pp. 247 - 252.
  43. V. Chitraa and Dr. Antony Selvdoss Davamani, "A Survey on Preprocessing Methods for Web Usage Data," International Journal of Computer Science and Information Security, Vol. 7, No. 3, 2010, pp. 78-83.
  44. Vivek Arvind. B Swaminathan. and J Viswanathan. K. R. , "An Improvised Filtering Based Intelligent Recommendation Technique for Web Personalization," In Proc. of Annual IEEE India Conference, 2011, pp. 1194 - 1199.
  45. Xiaoguang Qi and Brian D. Davison, "Web Page Classification: Features and Algorithms," ACM Computing Surveys, Vol. 41, No. 2, 2009, Article No. 12.
  46. Ya-min WANG, Xue-ling HAN and Xiao-wei LIU, "E-commerce Recommendation System Based on CBR and Web Log Mining," In Proc. of IEEE 18th International Conference on Industrial Engineering and Engineering Management, 2011, pp. 311-315.
  47. Yu-Hai tao , Tsung-Pei Hong and Yu-Ming Su, "Web usage mining with intentional browsing data," Expert Systems with Applications , Science Direct,2008.
  48. Yu-Hai tao, Tsung-Pei Hong and Yu-Ming Su, "Web usage mining with intentional browsing data," Expert Systems with Applications, Science Direct, 2008.
  49. Zhiwen Yu, Yuichi Nakamura, Seiie Jang, Shoji Kajita, and Kenji Mase, "Ontology-Based Semantic Recommendation for Context-Aware E-Learning," Springer, LNCS, Vol. 4611, 2007, pp. 898-907.
  50. Zi-Ke Zhang, Tao Zhou and Yi-Cheng Zhang "Tag-Aware Recommender Systems: A State-of-the-Art Survey," Springer, Journal of Computer Science and Technology, Vol. 26, No. 5, 2011, pp 767-777.
Index Terms

Computer Science
Information Sciences

Keywords

Web Usage mining Web Personalization Recommendation System

Powered by PhDFocusTM