Secure Personalized Recommendation System for Mobile User

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.