The Recommendation system relies on feedback and personal information collected from
users for effective recommendation. The success of a recommendation system is highly …

Recommendation systems rely heavily on behavioural and preferential data (eg, ratings and
likes) of a user to produce accurate recommendations. However, such unethical data …

Recommender systems aim at predicting users' future behaviors by learning the users'
personal information and historical behaviors. Unfortunately, training by the user's raw data …

Focusing on the privacy issues in recommender systems, we propose a framework
containing two perturbation methods for differentially private collaborative filtering to prevent …

Recommender Systems depend on massive amount of user data to provide accurate results.
Such dependency creates a threat to individual user privacy. In this paper, a differential …

Privacy risks of recommender systems have caused increasing attention. Users' private data
is often collected by probably untrusted recommender system in order to provide high …

Recently, item-based collaborative filtering has attracted a lot of attention. It recommends to
users new items which may be of interests to them, based on their reported historical data …

Collaborative filtering plays an essential role in a recommender system, which recommends
a list of items to a user by learning behavior patterns from user rating matrix. However, if an …

Recommender systems have been widely applied in many scenarios such as shopping
websites, online learning systems and video sharing clients, etc, which need to collect user's …

Recommender systems provide users personalized services by collecting and analyzing
interaction data, undermining user privacy to a certain extent. In federated recommender …