Open Access Open Access  Restricted Access Subscription or Fee Access

An Effective System for Web Page Recommendation Using Pattern Mining Algorithms

S. Uma Maheswari, Dr. S.K. Srivatsa

Abstract


Web usage mining is the application of data mining techniques to discover usage patterns from web data, in order to understand and better serve the needs of web-based applications. Web usage mining is parsed into three distinctive phases such as preprocessing, pattern discovery and pattern analysis. Analyzing data through web usage mining can help effective web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and etc.The aim of this paper is to give web page recommendations with help of preprocessed, analyzed web log data and user profile. In this paper combined effort of clustering and association rule mining is applied for pattern discovery which helps in finding effective usage patterns. This recommendation system provides recommendations based on user’s navigational patterns and gives suitable recommendations to cater to current needs of users. The experimental results performed on real usage data show a significant improvement in the recommendation effectiveness of the proposed system.


Keywords


Web Usage Mining, Web Log, Clustering, Association Rule Mining.

Full Text:

PDF

References


V.V.R. Maheswara Rao and Dr. V. Valli Kumari, “An Enhanced Pre-Processing Research Framework For Web Log Data Using A Learning Algorithm”, Netcom 2010,Cscp 01, Pp. 01–15, 2011.

Mr. Sanjay Bapu Thakare and Prof. Sangram. Z. Gawali, “A Effective and Complete Preprocessing for Web Usage Mining”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 03, 2010, 848-851.

Hussain.T, Asghar.S and Masood. N, “Web usage mining: A survey on preprocessing of web log file”,Information and emerging technologies,2010, ISBN: 978-1-4244-8001-2

Jiawai Han and Micheline Kamber,”Data mining-Concepts and echniques”, secondedition, Elsevier, Reprint 2010.

Mahmoud naghibzadeh, Mehrdad Jalali,” Web page recommendation based on semantic web usage mining”, Springer, LNCS 7710,socinfo 2012,PP: 293-405.

H.Yilmaz , P. Senkul,”Using ontology and sequence information for extracting behavior patterns from web navigation logs”, In Proceedings of the 2010 IEEE Int. Conf. on Data Mining Workshops, ICDMW ’10, pages 549–556.

S. Ganesan , A. Sivaneri, S. Selvaraju,” Evolving interest based user groups using PSO algorithm”, Recent Trends in Information Technology (ICRTIT), 2014 IEEE International Conference,PP:1 – 6.

G.Poorna Latha , S.Raghavendra Prakash, ”Clustering web page sessions using sequence alignment method”, CCIS 250,springer 2011,PP: 479-483.

S.Vargas, P. Castells,”Rank and relevance in novelty and diversity metrics for recommender Systems”, In Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pages 109-116.

R. Suguna, D. Sharmila Association Rule Mining for WebRecommendation, R. Suguna et al. / International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397 Vol. 4 No. 10 Oct 2012.

S. K. Pani, L. Panigrahy, and V.H.Sankar Web Usage Mining: A Survey on Pattern Extraction from Web Logs, International Journal of Instrumentation, Control & Automation (IJICA), Volume 1, Issue 1,2011.

Narendra Sharma , Aman Bajpai , Mr. Ratnesh Litoriya Comparison the various clustering algorithms of weka tools, International Journal of Emerging Technology and Advanced Engineering, (ISSN 2250-2459,Volume 2, Issue 5, May 2012)

Haitao Zou, Zhiguo Gong, Nan Zhang, Wei Zhao, Jingzhi Guo,”Trust-Rank: A Cold-Start Tolerant Recommender System”, Enterprise Information Systems, volume 9, issue 2, 2015, pages 117-138.

Bahram Amini, Roliana Ibrahim,Mohd Shahizan Othman,Mohammad Ali Nematbakhsh,” A reference ontology for profiling scholar’s background knowledge in recommender systems”,Expert Systems with Applications,Volume 42,Issue 2,2015,PP:913-928,Elsevier Ltd.

J.Gupta, J.Gadge,” Performance analysis of recommendation system based on collaborative filtering and demographics,Communication”, Information & Computing Technology (ICCICT), 2015 IEEE International Conference , PP:1-6,ISBN: 978-1-4799-5521-3.

J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez,”Recommender systems survey”, Knowledge-Based Systems 46 (Elsevier) 2013,PP: 109–132.

Xujuan Zhou, Yue Xu, Yuefeng Li, Audun Josang and Clive Cox,” The State-of-the-Art in Personalized Recommender Systems for Social Networking”, Artificial Intelligence Review, Springer 2012.

vinti agarval,k.k.Bharadvaj,”A collaborative filtering framework for friends recommendation in social network based on interaction intensity and adaptive user similarity”, springer 2012,PP: 359-379.

Ido Guy and David Carmel,” Social Recommender Systems” ACM, ISBN: 978-1-4503-0637, 2011, PP: 283-284.

Bart P. Knijnenburg, Martijn C. Willemsen ,Zeno Gantner , Hakan Soncu, Chris Newell,” Explaining the user experience of recommender systems”,User modeling and User adapted interaction,Springer 2012,Volume 22 Issue 4-5,Pages:441-504.

Rajhans Mishra, Pradeep Kumar,Bharat Bhasker, ,”A web recommendation system considering sequential information, Decision Support Systems” (Elsevier) ,Volume 75,2015,PP:1-10.

R Forsati, A Moayedikia, M Shamsfard,” An effective Web page recommender using binary data clustering”, Information Retrieval Journal, 2015(Springer),Volume 18,PP:167-214.

Modi, H.Y. Dwarkadas J. Narvekar, M.,”Enhancement of online web recommendation system using a hybrid clustering and pattern matching approach”, Nascent Technologies in the Engineering Field (ICNTE), 2015 IEEE International Conference, ISBN:978-1-4799-7261-6, PP: 1 – 6.

C. Martinez-Cruz, C. Porcel,”A model to represent users trust in recommender systems using ontology and fuzzy linguistic modeling”, Information Sciences(Elsevier), Volume 311, 2015,Pages 102-118.

María N. Moreno,Saddys Segrera,Vivian F. López,María Dolores Muñoz,Ángel Luis Sánchez,”Web mining based framework for solving usual problems in recommender systems: A case study for movies׳ recommendation”, Neurocomputing(Elsevier),volume 176,2015,PP:72-80.

Sanjeev Kumar Sharma, Ugrasen Suman,” An efficient semantic clustering of URLs for web page recommendation”, International journal of data analysis Techniques and strategies 2013, volume 5.Issue 4, PP: 339-358.

M. Maged, M. Deghaidy, Khaled Mahmoud Badran ,Gouda Ismail Mohamed, ” Web Recommendation Framework based on Association Rules Coverage to be Applied for Site Modification” ,International Journal of Computer Applications (0975 – 8887), Volume 91 – No 2, 2014 ,PP:27-33.

R. Suguna, D. Sharmila ,”An Efficient Web Recommendation System using Collaborative Filtering and Pattern Discovery Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 70– No.3, 2013,PP:36-44.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.