Journal Title : International Journal of Modern Trends in Engineering and Science
Volume 03 Issue 07 2016
ISSN no: 2348-3121
Page no: 54-58
Abstract – This paper is aimed to provide Personalized Web Page Recommendation for anonymous web users using Domain Ontology and Conceptual Prediction. Web usage mining is the process of extracting knowledge from web user’s access by using data mining technologies. This recommender system is to improve Web site usability. Web usage mining prediction process is structured according to web server activity and analyzing historical data such as server access log file or web logs which are captured from the server then these web logs are used capturing the intuition list of the user so as to recommend page views to the user whenever he/she comes online for the next time. This present architecture for capturing recommendations in the form of intuition list of user. Intuition list consist of list of pages visited by user as well as the list of pages visited by other user of having similar usage profile. To developed and implemented personalized-recommendation system, a system that makes use of representations of items and user-profiles based on ontologies in order to provide semantic applications with personalized services.
Keywords— Conceptual Prediction, Domain, Mining, Extraction, Intuition
- Borges J. and Levene M. (2005), ‘Generating Dynamic Higher-Order Markov Models in Web Usage Mining’, in Proc. PKDD, Porto, Portugal, pp. 34–45.
- Bose A., Beemanapalli K., Srivastava J., and Sahar S. (2006), ‘Incorporating Concept Hierarchies into Usage Mining Based Recommendations’, in Proc. 8th WebKDD, Philadelphia, PA, USA, pp. 110–126.
- Brindha S. and Sabarinathan P. (2014), ‘An Effective Search on Web Log from Most Popular Downloaded Content’, International Journal of Distributed and Parallel Systems (IJDPS) Vol. 5, No.1, pp. 51–57.
- Dai P. A., Scime A., Hershey Ed. and Mobasher B. (2005), ‘Integrating Semantic Knowledge with Web Usage Mining for Personalization’, in Web Mining:Applications and Techniques, USA: IGI Global, pp. 205–232.
- Ezeife I. and Lu. Y. (2005), ‘Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree’, Data Mining. Knowledge. Disc., Vol. 10, No. 1, pp. 5–38.
- José M. Gascueña, Antonio Fernández-Caballero and Pascual González. (2009), ‘Personalized Web Search with Location Preferences’, SIGIR Forum, Vol. 3, No. 1, pp. 1-17.
- Mabroukeh N. R. and Ezeife C. I. (2009), ‘Semantic-rich Markov Models for Web Prefetching’, in Proc. ICDMW, Miami, FL, USA, pp. 465–470.
- Mahendra Pratap Singh Dohare, Premnarayan Arya and Aruna Bajpai. (2012), ‘Novel Web Usage Mining for Web Mining Techniques International Journal of Emerging Technology and Advanced Engineering’, Vol. 2, Issue 1, pp. 253–262.
- Mahony O., Hurley N., Kushmerick N., and Silvestre G. (2004), ‘Collaborative Recommendation: A Robustness Analysis’, ACM Trans. Internet Technol., Vol. 4, No. 4, pp. 344–377.
- Nguyen T. T. S., Lu H., Tran T. P., and Lu J. (2012), ‘Investigation of Sequential Pattern Mining Techniques for Web Recommendation’, Int. J. Inform. Decis. Sci., Vol. 4, No. 4, pp. 293–312.
- Olfa Nasraoui, Esin Saka, Antonio Badia and Richard Germain. (2008), ‘A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites’, in IEEE transactions on knowledge and data engineering, Vol. 20, No. 2, pp. 1465–1470.
- Rios S. A. and Velasquez J. D. (2008), ‘Semantic Web Usage Mining by a Concept-Based Approach for Off-Line Web Site Enhancements’, in Proc. WI-IAT, Sydney, NSW, Australia, pp. 234–241.