The week's pick
Reseach Article
A New Method to Create the Profile and Improving the Queries in Web
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 88 - Number 13 |
| Year of Publication: 2014 |
| Authors: Hale Falakshahi, Ali Harounabadi, Majid Mazinani |
10.5120/15414-3918
|
Hale Falakshahi, Ali Harounabadi, Majid Mazinani . A New Method to Create the Profile and Improving the Queries in Web. International Journal of Computer Applications. 88, 13 ( February 2014), 24-29. DOI=10.5120/15414-3918
Abstract
Finding needed information among the existing information on the web can be very time consuming and difficult. To tackle this problem, web personalization systems have been proposed that adapt the contents and services of web sites based on the users' interests. Studying users' behaviors in the past with web usage mining techniques utilization can be worthy help in personalization affair. Web servers log files considered as a rich resource for finding users' behavioral patterns. In this paper, users' behavioral patterns are obtained from studying of users' access and web usage mining utilization, especially users clustering. One of the innovative aspects of the research is selecting some behavioral features from users. These features include the 'pages view', 'page view frequency', 'time period of viewing the pages' and ' order of viewing the pages' which are stored in users' profiles. In addition is considered weight criterion for the first three features. Thus clustering has been done by considering this criterion with K-means algorithm. Neural network usage is another feature of proposed system to form recommender engine, which its function is to find proper behavioral pattern for users' session and forecast upcoming demands. As research conclusion presents recommender engine has the appropriate accuracy in prediction of user's inquiry.
References
- Zheng, W. , & Zhang, M. (2011, September). The investigation for Web user clustering based on interest. In Electronics, Communications and Control (ICECC), 2011 International Conference on (pp. 553-556). IEEE.
- Agosti, M. , & Di Nunzio, G. M. (2007). Gathering and mining information from web log files. In Digital Libraries: Research and Development (pp. 104-113). Springer Berlin Heidelberg.
- Xu, J. , & Liu, H. (2010, October). Web user clustering analysis based on KMeans algorithm. In Information Networking and Automation (ICINA), 2010 International Conference on (Vol. 2, pp. V2-6). IEEE.
- Yan, T. W. , Jacobsen, M. , Garcia-Molina, H. , & Dayal, U. (1996). From user access patterns to dynamic hypertext linking. Computer Networks and ISDN Systems, 28(7), 1007-1014.
- Srivastava, J. , Cooley, R. , Deshpande, M. , & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 1(2), 12-23.
- Mobasher, B. , Dai, H. , Luo, T. , & Nakagawa, M. (2001, November). Effective personalization based on association rule discovery from web usage data. InProceedings of the 3rd international workshop on Web information and data management (pp. 9-15). ACM.
- Yang, Q. , Zhang, H. H. , & Li, T. (2001, August). 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.
- Li, I. T. Y. , Yang, Q. , & Wang, K. (2001, May). Classification Pruning for Web-request Prediction. In WWW Posters.
- Mobasher, B. , Cooley, R. , & Srivastava, J. (1999). Creating adaptive web sites through usage-based clustering of URLs. In Knowledge and Data Engineering Exchange, 1999. (KDEX'99) Proceedings. 1999 Workshop on (pp. 19-25). IEEE.
- Perkowitz, M. , & Etzioni, O. (1998, July). Adaptive web sites: Automatically synthesizing web pages. In AAAI/IAAI (pp. 727-732).
- Cooley, R. W. (2000). Web usage mining: discovery and application of interesting patterns from web data (Doctoral dissertation, University of Minnesota).
- Nasraoui, O. , & Krishnapuram, R. (2002). An evolutionary approach to mining robust multi-resolution web profiles and context sensitive URL Associations. International Journal of Computational Intelligence and Applications, 2(03), 339-348.
- Xie, Y. , & Phoha, V. V. (2001, October). Web user clustering from access log using belief function. In Proceedings of the 1st international conference on Knowledge capture (pp. 202-208). ACM.
- Petridou, S. G. , Koutsonikola, V. A. , Vakali, A. I. , & Papadimitriou, G. I. (2008). Time-aware web users' clustering. Knowledge and Data Engineering, IEEE Transactions on, 20(5), 653-667.
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