The increasing popularity of social media has attracted a huge number of people to
participate in numerous activities on a daily basis. This results in tremendous amounts of …

The popularity of online recommender systems has soared; they are deployed in numerous
websites and gather tremendous amounts of user data that are necessary for …

Recommender systems rely on personal information about user behavior for the
recommendation generation purposes. Thus, they inherently have the potential to hamper …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …

This article proposes a new technique for Privacy Preserving Collaborative Filtering (PPCF)
based on microaggregation, which provides accurate recommendations estimated from …

In this paper, we propose a new privacy solution for the data used to train a recommender
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …

Collaborative recommender systems offer a solution to the information overload problem
found in online environments such as e-commerce. The use of collaborative filtering, the …

The increasing popularity of social media has attracted a huge number of people to
participate in numerous activities on a daily basis. This results in tremendous amounts of …

Conventional approaches to matrix factorisation (MF) typically rely on a centralised
collection of user data for building a MF model. This approach introduces an increased risk …

Due to the rapid growth of Internet, E-commerce and Internet of Things, people use Web
based services for most of their needs including buying items, reading books, watching …