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User Next Web Page Recommendation using Weight based Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 142 - Number 11 |
| Year of Publication: 2016 |
| Authors: Arvind Verma, Balwant Prajapat |
10.5120/ijca2016909899
|
Arvind Verma, Balwant Prajapat . User Next Web Page Recommendation using Weight based Prediction. International Journal of Computer Applications. 142, 11 ( May 2016), 49-53. DOI=10.5120/ijca2016909899
Abstract
The World Wide Web is a source of knowledge; the knowledge is extracted from the web data. Web data is available in direct from normal web as contents to user and/or in direct forms to as the web access logs. For the web usage pattern analysis the web access logs are analysed. Web usage data used in various applications of web masters, user data recommendations, web pre-fetching and caching. In this paper using the web access log analysis, web next page recommendation system is introduced. The presented technique involves data personalization, user behavioural analysis and access patterns for recommendations. The proposed web page recommendation system contains the K-means algorithm for finding similar access patterns of the user sessions. Additionally for classification and prediction the KNN algorithm is implemented. The model also incorporate the similar user access pattern data which is belongs from the other user therefore the proposed model also predicts the rarely accessed patterns. Thus to make the recommendations web usages data is personalized, based on URL frequencies, user navigational frequencies, session based data analysis and time based data analysis. Additionally to combine these parameters a weighted technique is used. The proposed recommendation system is implemented using JAVA technology. And their performance in terms of accuracy, error rate, space complexity and time complexity is estimated. The experimentation with increasing amount of data provides more accurate results and also consumes less computational resources. Therefore the proposed data model is adoptable for accuracy and efficiency both.
References
- Lina Yao and Quan Z. Sheng, Aviv Segev, Jian Yu, “Recommending Web Services via Combining Collaborative Filtering with Content-based Features”, 2013 IEEE 20th International Conference on Web Services, 978-0-7695-5025-1/13 $26.00 © 2013 IEEE
- Rana Forsati, Mohammad Reza Meybodi, Afsaneh Rahbar, “An Efficient Algorithm for Web Recommendation Systems”, 2009 IEEE/ACS International Conference on Computer Systems and Applications
- Kavita Sharma, Gulshan Shrivastava, Vikas Kumar, “Web Mining: Today and Tomorrow”, 2011 3rd International Conference on Electronics Computer Technology (ICECT 2011), 978-1-4244-8679-3/$26.00 2011 IEEE
- Rajni Pamnani, Pramila Chawan, “Web Usage Mining: A Research Area in Web Mining”, Proceedings of ISCET 2010.
- Quanyin Zhu, Hong Zhou, Yunyang Yan, Jin Qian and Pei Zhou, “Commodities Price Dynamic Trend Analysis Based on Web Mining”, 2011 Third International Conference on Multimedia Information Networking and Security, 978-0-7695-4559-2/11 $26.00 © 2011 IEEE
- K. Srinivas, P. V. S. Srinivas, A. Govardhan, V. Valli Kumari, “Periodic Web Personalization for Meta Search Engine”, IJCST Vol. 2, Issue 4, Oct. - Dec. 2011
- Bussa V. R. R. Nagarjuna, Akula Ratna babu, Miriyala Markandeyulu, A. S. K. Ratnam, “Web Mining: Methodologies, Algorithms and Applications”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-2, Issue-3, July 2012
- Sneha Prakash, “Web Personalization using web usage mining: applications, Pros and Cons, Future”, International Journal of Computing Science and Information Technology, 2015, Vol.3,Iss.3, 18-26
- Neha Sharma & Pawan Makhija, “Web usage Mining: A Novel Approach for Web user Session Construction”, Global Journal of Computer Science and Technology: E Network, Web & Security Volume 15 Issue 3 Version 1.0 Year 2015
- D.A. Adeniyi, Z. Wei, Y. Yongquan, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method”, Saudi Computer Society, King Saud University, Applied Computing and Informatics, 2015 Production and hosting by Elsevier B.V
- Haidong Zhong, Shaozhong Zhang, Yanling Wang, Shifeng Weng and Yonggang Shu, “Mining Users’ Similarity from Moving Trajectories for Mobile Ecommerce Recommendation”, International Journal of Hybrid Information Technology Vol.7, No.4 (2014), pp.309-320
- A. Tejeda-Lorente, C. Porcel, E. Peisc, R. Sanz, E. Herrera-Viedma, “A quality based recommender system to disseminate information in a University Digital Library”, Information Sciences (2013),
- Renuka Mahajan, J. S. Sodhi, Vishal Mahajan, “Web Usage Mining for Building an Adaptive e-Learning Site: A Case Study”, International Journal of e-Education, e-Business, e-Management and e-Learning, Manuscript submitted July 10, 2014; accepted August 29, 2014.
- Zahid Ansari, A. Vinaya Babu, Waseem Ahmed and Mohammad Fazle Azeem, “A Fuzzy Set Theoretic Approach to Discover User Sessions from Web Navigational Data”, IEEE Recent Advances in Intelligent Computational Systems (RAICS) 2011, 978-1-4244-9478-1/11/$26.00 c 2011 IEEE
- Ricardo Terra, Marco Tulio Valente, Krzysztof Czarnecki and Roberto S. Bigonha, “A recommendation system for repairing violations detected by static architecture conformance checking”, SOFTWARE – PRACTICE AND EXPERIENCE, Softw. Pract. Exper. 2015; 342, Published online 25 September 2013.
- I. Petrovic, P. Perkovic and I. Štajduhar, “A Profile- and Community-Driven Book Recommender System”, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), MIPRO 2015/CTI.
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