Recommender systems are an integral part of modern-day user experience. They
understand their preferences and support them in discovering meaningful content by …

Recommender systems are increasingly becoming an integral part of on-line services. As
the recommendations rely on personal user information, there is an inherent loss of privacy …

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 …

Point-of-Interest (POI) recommendation has been extensively studied and successfully
applied in industry recently. However, most existing approaches build centralized models on …

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

Non-negative matrix factorization (NMF) with missing-value completion is a well-known
effective Collaborative Filtering (CF) method used to provide personalized user …

This paper proposes a privacy-preserving distributed recommendation framework, Secure
Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and …

This chapter studies the relationship between two important, often conflicting paradigms of
online services: personalization and tracking. The chapter initially focuses on the categories …

Social information access (SIA) systems crucially depend on user-provided information, and
must therefore provide extensive privacy provisions to encourage users to share their …

Electronic content is ubiquitous in our daily lives. Several factors such as the development of
Web 2.0 technologies, the increased access to mobile devices and the deployment of …