The Research Of Privacy Issues In Recommender System

The growing use of recommender systems online,social networks present a privacy risk for many users and data sparsity.This thesis identifies the various privacy aspects prevailing in the movie's recommendation domain.Movies personalization has become crucial on the web as the user shows more interest to stay updated with the trend of the current movies within a limited period.The quality and accuracy of such personalized movies recommendation rely on leveraging user profiles of the movies.For more generic movie recommendation,the system collects user click history and page access pattern implicitly.The need and association of user-profiles give rise to privacy concerns in the movie's domain,whereas privacy of user identity,user behavior in terms of page access patterns contributes to the overall privacy risks in the movie's domain.In our research,we focus to deeply investigate the current state of art recommender system in specific domain,Security risks,user Privacy issues,privacy policies as well as characteristics and collected real time user feedback about privacy,preference,trust and ownership.For an online business,recommendation systems have become an extremely effective revenue driver and developed rapidly.Although recommendation systems are greatly beneficial,directly exposing privacy data to the recommender may lead to leakage of privacy and data sparsity.In this thesis we introduce a new methodology to control the data sparsity and data privacy for the new user.Finally,user-based research was carried out via a survey questionnaire designed to collect the online users 'privacy-centered opinions.It is found that privacy preferences,knowledge,and ownership(control)of the user over their own data can significantly influence the privacy concerns of the online user.Furthermore,the study of the results of the survey reveals that most of the users have much concern about online privacy.