Abstract
Nowadays, due to the rapid growth of the mobile users, personalization and recommender systems have gained popularity. The recommender systems serve the personalized information to the users according to user preferences or interests and their profiles. Tourism is an industry which had adopted the use of new technologies. Recently, mobile tourism has come into spotlight. Due to the rapid growing of user needs in mobile tourism domain, we concentrated on to gives the personalized recommendation based on multi-agent technology in tourism domain to serve the mobile users [7]. The objective of this paper is to build a secure personalized recommendation system. Attackers can affect the prediction of the recommender system by injecting a number of biased profiles. In this paper, we consider detecting or preventing the profile injection (also called shilling attacks) by using significant weighting and trust weighting that complements to our proposed RPCF Algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aïmeur, E., Brassard, G., Fernandez, J.M., Onana, F.S.M.: Alambic: A Privacy-Preserving Recommender System for Electronic Commerce. International Journal of Information Security 7(5), 307–334 (2008)
Burke, R., Mobasher, B., Zabicki, R., Bhaumik, R.: Identifying Attack Models for Secure Recommendation. In: Beyond Personalization: A Workshop on the Next Generation of Recommender Systems, San Diego, California (2005)
Cheng, Z., Hurley, N.: Analysis of Robustness in Trust-based Recommender Systems. In: RIAO 2010, Paris, France, Copyright CID (2010)
Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. ACM 35(12), 61–70 (1992)
Lam, S.K., Riedl, J.: Shilling Recommender Systems for Fun and Profit. In: 13th International World Wide Web Conference (WWW 2004), New York, NY, USA, pp. 393–402 (2004)
Loh, S., Lorenzi, F., Saldana, R., Licthnow, D.: A Tourism Recommender System based on Collaboration and Text Analysis. Information Technology & Tourism 6, 157–165 (2004)
Maw, S.Y., Thein, N.L.: Multi-Agent Mobile Tourism System. Encyclopedia of Information Science and Technology. In: Information Science Reference, 2nd edn., vol. VI (Mu-Q), pp. 2722–2727. IGI Global Publishing, Hershey (2008)
Maw, S.Y., Naing, M.-M., Thein, N.L.: RPCF Algorithm for Multi-Agent Tourism System. In: IEEE International Symposium on Micro-NanoMachatronics and Human Science (MHS 2006), Nagoya, Japan, pp. 533–538 (2006)
Maw, S.Y., Naing, M.-M.: Multi-Agent Tourism System (MATS). In: Social Information Retrieval System: Emerging Technologies and Applications for Searching the Web Effectively. Ch. XV, pp. 289–310. IGI Global Publishing, Hershey (2007)
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Effective Attack Models for Shilling Item-based Collaborative Filtering Systems. In: WebKDD Workshop (2005)
Mobasher, B., Burke, R., Bhaumik, R., Sandvig, J.: Attacks and Remedies in Collaborative Recommendation. IEEE Intelligent Systems 22(3), 56–63 (2007)
O’Donovan, J., Smyth, B.: Trust in Recommender Systems. In: 10th International Conference on Intelligent User Interfaces (IUI 2005), pp. 167–174. ACM Press, San Diego (2005)
Quan, Q., Hinze, A.: Trust-based Recommendations for Mobile Tourists in TIP. Working Paper: 13/2008, Hamilton, New Zealand (2008)
Sammut, C., Webb, G.: Encyclopedia of Machine Learning. Ch. 00338, pp. 1–9. Springer, Heidelberg (2010)
Shon, T., Choi, W.: An Analysis of Mobile WiMAX Security: Vulnerabilities and Solutions. In: Enokido, T., Barolli, L., Takizawa, M. (eds.) NBiS 2007. LNCS, vol. 4658, pp. 88–97. Springer, Heidelberg (2007)
Web site personalization, http://www.128.ibm.com/developerworks/websphere/library/techarticles/hipods/personalize.html
Williams, C., Bhaumik, R., Burke, R., Mobasher, B.: The Impact of Attack Profile Classification on the Robustness of Collaborative Recommendation. In: WebKDD Workshop, ACM SIGKDD Conference on Data Mining and Knowledge Discovery (KDD 2006), Philadelphia (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maw, S.Y. (2011). Secure Personalized Recommendation System for Mobile User. In: Rhee, KH., Nyang, D. (eds) Information Security and Cryptology - ICISC 2010. ICISC 2010. Lecture Notes in Computer Science, vol 6829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24209-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-24209-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24208-3
Online ISBN: 978-3-642-24209-0
eBook Packages: Computer ScienceComputer Science (R0)