Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
Attacks and Remedies in Collaborative Recommendation
IEEE Intelligent Systems
Alambic: a privacy-preserving recommender system for electronic commerce
International Journal of Information Security
An analysis of mobile WiMAX security: vulnerabilities and solutions
NBiS'07 Proceedings of the 1st international conference on Network-based information systems
Analysis of robustness in trust-based recommender systems
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Encyclopedia of Machine Learning
Encyclopedia of Machine Learning
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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.