A new algorithm to create a profile for users of web site benefiting from web usage mining

masomeh khabazfazli, ali harounabadi, shahram jamali

Abstract


Upon integration of internet and its various applications and increase of internet pages, access to information in search engines becomes difficult. To solve this problem, web page recommendation systems are used. In this paper, recommender engine are improved and web usage mining methods are used for this purpose. In recommendation system, clustering was used for classification of users’ behavior. In fact, we implemented usage mining operation on the data related to each user for making its movement pattern. Then, web pages were recommended using neural network and markov model. So, performance of recommendation engine was improved using user’s movement patterns and clustering and neural network and Markov model, and obtained better results than other methods. To predict the data recovery quality on web, two factors including accuracy and coverage were used


Keywords


Web Page Recommendation; Web Mining; Web Usage Mining; Clustering; Neural Network; markov model

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References


A. Peña-Ayala, “Educational data mining: A survey and a data mining-based analysis of recent works, Journal of Expert Systems with Applications”, Vol. 41, No. 4, Part 1, March 2014, pp.1432-1462.

K., Santhisree, A., Damodaram, “Clustering on Web usage data using Approximations and Set Similarities, International Journal of Computer Applications”, Vol. 1, No. 4, , 2010, pp.0975 – 8887.

Ida Mele, “Web Usage Mining for Enhancing Search-Result Delivery and Helping Users to Find Interesting Web Content”, ACM, WSDM’13, February 2013, pp.765-769.

J., Jose, P., Sojan Lal, “Extracting Extended Web Logs to Identify the Origin of Visits and Search Keywords”, Intelligent Informatics Advances in Intelligent Systems and Computing, Vol. 182, 2013, pp.435-441.

G., Castellano, A. M., Fanelli, M. A., Torsello, “NEWER: A system for Neural-fuzzy Web Recommendation”, journal of Applied Soft Computing, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, vol. 11, No. 1, 2010, pp.793-806.

X., Dongshan, S., Junyi, “A New Markov Model for Web Access Prediction”, journal of Computing in Science and Engineering, vol. 4, no. 6, 2002, pp. 34-39.

K., Goseva-Popstojanova, G., Anastasovski, A., Dimitrijevikj, R., Pantev, B., Miller, “Characterization and classification of malicious Web traffic, Journal of Computers & Security”, Vol. 42 , May 2014, pp.92-115.

Y. S., Cho, S. C., Moon, S., Jeong, I., Oh, , K., Ho Ryu , “Clustering Method Using Item Preference Based on RFM for Recommendation System in U-Commerce”, Ubiquitous Information Technologies and Applications, Vol. 214, 2013, pp.353-362.

Z., khademali, A., harounabadi, J., mirabedini, “A new intelligent algorithm to creat a profile for user based on web interaction”, Journal of management science letters, vol. 3, no.4, 2013, pp. 1155-1160.




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