Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems
in industry, owing to its strength in user interest modeling and ease in online …

Recommender systems use intelligent algorithms to learn a user's preferences and provide
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …

Most of the existing recommender systems understand the preference level of users based
on user-item interaction ratings. Rating-based recommendation systems mostly ignore …

The rapid development of location-based social networks (LBSNs) provides a substantial
amount of image data which not only reveals visual contents of POIs but also users' visual …

Recommending user generated lists (eg, playlists) has become an emerging task in many
online systems. Many existing list recommendation methods predict user preferences on …

Personalized recommendation of film and television culture is an important content to meet
people's daily cultural needs and social information. Promoting the personalized …

Despite being studied for more than twenty years, state-of-the-art recommendation systems
still suffer from important drawbacks which limit their usage in real-world scenarios. Among …

With the ever-growing scale of social websites and online transactions, in past decade,
Recommender System (RS) has become a crucial tool to overcome information overload …

Recent years have witnessed the explosive growth of interaction behaviors in multimedia
information systems, where multi-behavior recommender systems have received increasing …

In this paper, we propose a secure and privacy-preserving technique for computing dot-
product kernels on vertically distributed data. Our proposal is based on secure multi-party …