Intelligence Based User Profile Generation

Abstract:

Web intelligence provides a platform that empowers internet users to determine the most appropriate and best information for their interests. It provides the ability to sense and adapt to the needs and preference of the user. The recent advancements have made it conceivable to capture the users experience and interactions with web. Consequently predicting users behaviors will expedite and enhance browsing experience. This paper proposes an intelligent approach for making the web more powerful by predicting the conduct of individual users. The main goal is to implicitly construct user profiles using a Particle Swarm Optimization - based technique. We reveal interesting results in comparing with a standard user modeling approach.

• Periodical: Applied Mechanics and Materials (Volume 573)
• Pages: 618-623
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.573.618
• Online since: June 2014
• Authors: Mani Ambika*, K. Latha
• Keywords: User Modeling, User Personalization, User Profile, Web Mining, Web Usage Mining

References

[1] Honghua Dai, Bamshad Mobasher, A road map to more web personalization: Integrating Domain Knowledge with web usage mining.

[2] Gabriella Pasi, Issue in personalizing information retrieval, IEEE Intelligent InformaticsBulletin, Vol. 11, no. 1, (2010).

[3] Zhong, Ning, Liu Yao, Jiming, Yao, Y.Y. Ohsuga, S., Web Intelligence (WI), Web Intelligence, Computer Software and Applications Conference, The 24th Annual International, p.469, doi: 10. 1109/CMPSAC. 2000. 884768, ISBN 0-7695-0792-1, (2000).

DOI: 10.1109/cmpsac.2000.884768

[4] Micro Speretta, Susan Gauch, Personalized Search Based on User Serach Histories, Proceesings of IEEE /WIC /ACM International Conference on Web Intelligence, (2005).

DOI: 10.1109/wi.2005.114

[5] Susan Gauch, Mirco Speretta, Aravind Chandramouli, and Alessandro Micarelli. User profiles for personalized information access. pages 54-89, (2007).

DOI: 10.1007/978-3-540-72079-9_2

[6] Omran M, Engelbrecht AP and Salman A, Particle Swarm Optimization Method for Image Clustering, International Journal of Pattern Recognition and Artificial Intelligence, 19(3), pp.297-322, (2005).

DOI: 10.1142/s0218001405004083

[7] Shafiq Alam, Gillian Dobbie, Patricia Riddle, Particle Swarm Optimization Based Clustering Of Web Usage Data, IEEE/ WIC/ ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp.451-454, (2008).

DOI: 10.1109/wiiat.2008.292

[8] Ajith Abraham, Swagatam Das, and Sandip Roy, Swarm Intelligence Algorithms for Data Clustering.

[9] Lu Dai, Wei Wang and Wanneng Shu, An Efficient Web Usage Mining Approach Using Chaos Optimization and Particle Swarm Optimization Algorithm Based on Optimal Feedback Model, Mathematical Problems in Engineering , Volume 2013, Article ID 340480.

DOI: 10.1155/2013/340480

[10] Sarabjot Singh Anand and Bamshad Mobasher, Intelligent Techniques for Web Personalization, LNAI 3169, p.1–36, Springer-Verlag Berlin Heidelberg, (2005).

[11] Alam, S., G. Dobbie, et al., Particle Swarm Optimization Based Clustering Of Web Usage Data. IEEE/ WIC/ ACM International Conference on Web Intelligence and Intelligent Agent Technology, DOI 10. 1109, (2008).

DOI: 10.1109/wiiat.2008.292