CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Importance of Domain Knowledge in Web Recommender Systems

by Saloni Aggarwal, Veenu Mangat
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 16
Year of Publication: 2015
Authors: Saloni Aggarwal, Veenu Mangat
10.5120/ijca2015906643

Saloni Aggarwal, Veenu Mangat . Importance of Domain Knowledge in Web Recommender Systems. International Journal of Computer Applications. 127, 16 ( October 2015), 10-14. DOI=10.5120/ijca2015906643

@article{ 10.5120/ijca2015906643,
author = { Saloni Aggarwal, Veenu Mangat },
title = { Importance of Domain Knowledge in Web Recommender Systems },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 16 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number16/22812-2015906643/ },
doi = { 10.5120/ijca2015906643 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:18:11.344940+05:30
%A Saloni Aggarwal
%A Veenu Mangat
%T Importance of Domain Knowledge in Web Recommender Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 16
%P 10-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web usage data is extensively used in every domain to analyze the browsing behavior of the users who visited the website or search engines. Web usage data is contained in server logs called as Web Logs. This data enables the website owners to infer the needs and interests of the users for using this information to increase the revenue from their web business. The website owners employ recommender systems for this purpose. The recommender systems exploit web usage data to predict what web pages the user will visit next and therefore offer the recommendations for those very pages to the user and offers them support while browsing. This in turn helps users to have a better browsing experience, personalized support and hence, probability of user buying out the products from that website increases. Web usage mining alone is used in traditional recommender systems. Modern recommender systems employ semantic knowledge base i.e. domain knowledge in addition to web usage mining for efficient prediction of pages as this helps in avoiding the new page problem. This paper presents a comparative and comprehensive study of modern and traditional recommender systems.

References
  1. Kosala, Raymond, and Hendrik Blockeel. "Web mining research: A survey." ACM Sigkdd Explorations Newsletter 2, no. 1 (2000): 1-15.
  2. Dai, Honghua, and Bamshad Mobasher. "A Road Map to More Effective Web Personalization: Integrating Domain Knowledge with Web Usage Mining." In International Conference on Internet Computing, pp. 58-64. 2003.
  3. Kazienko, Przemyshaw, and Maciej Kiewra. "Integration of relational databases and Web site content for product and page recommendation." In Database Engineering and Applications Symposium, 2004. IDEAS'04. Proceedings. International, pp. 111-116. IEEE, 2004.
  4. Lu Chen, Qiang Su, “Discovering User's Interest at E-Commerce Site Using Clickstream Data,” 10th International Conference on Service Systems and Service Management (ICSSSM) IEEE 2013, pp. 124-129, July 2013
  5. Omer Adel Nasser, Dr. Nedhal A. Al Saiyd, “The Integrating Between Web Usage Mining and Data Mining Techniques,” 5th International Conference on Computer Science and Information Technology, pp. 243-247, 2013.
  6. Yanduo Zhao, “The Review of Web Mining in E-commerce,” Proceeding ICCIS’13 Proceedings of the 2013 International Conference on Computational and Information Sciences, pp. 571-574, 2013..
  7. Anupma Surya, Dilip Kumar Sharma, “An approach for web page ordering using user session”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies(ICT), pp. 1009-1013, 2013.
  8. Kenneth Wai-Ting Leung, Dik Lun Lee, and Wang-Chien Lee, “PMSE: A Personalized Mobile Search Engine,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 4, pp. 820-824, April 2013.
  9. Ting, IHsien. "Web mining techniques for on-line social networks analysis." In Service Systems and Service Management, 2008 International Conference on, pp. 1-5. IEEE, 2008.
  10. Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval 2, no. 1-2 (2008): 1-135.
  11. Yang, Qiang, Haining Henry Zhang, and Tianyi Li. "Mining web logs for prediction models in WWW caching and prefetching." In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 473-478. ACM, 2001.
  12. Spiliopoulou, Myra, Carsten Pohle, and Lukas C. Faulstich. "Improving the effectiveness of a web site with web usage mining." In Web Usage Analysis and User Profiling, pp. 142-162. Springer Berlin Heidelberg, 2000.
  13. Wichian Premchaiswadi, Walia Romsaiyud, “Extracting WebLog of Siam University for Learning User Behavior on MapReduce,” 2012 4th International Conference on Intelligent and Advanced Systems(ICIAS), vol. 1, pp.149-154, 2012
  14. Nagarnaik, Paritosh, and A. Thomas. "Survey on recommendation system methods." In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on, pp. 1496-1501. IEEE, 2015.
  15. Thi Thanh Sang Nguyen, Hai Yan Lu, and Jie Lu, “Web-Page Recommendation Based on Web Usage and Domain Knowledge,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, Oct 2014.
  16. Venu Gopalachari, M., and P. Sammulal. "Personalized collaborative filtering recommender system using domain knowledge." International Conference on Computer and Communications Technologies (ICCCT) IEEE, pp. 1-6, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Semantic Knowledge Domain Knowledge Web Usage Data Personalized Services.

Powered by PhDFocusTM